Submodule containing all predefined schedulers of Agents.jl and the scheduling API. Schedulers have a very simple interface. They are functions that take as an input the ABM and return an iterator over agent IDs. Notice that this iterator can be a "true" iterator (non-allocated) or can be just a standard vector of IDs. You can define your own scheduler according to this API and use it when making an AgentBasedModel. You can also use the function schedule(model) to obtain the scheduled ID list, if you prefer to write your own step!-like loop.

See also Advanced scheduling for making more advanced schedulers.

Notice that schedulers can be given directly to model creation, and thus become the "default" scheduler a model uses, but they can just as easily be incorporated in a model_step! function as shown in Advanced stepping. The scheduler that is stored in the model is only meaningful if an agent-stepping function is defined for step! or run!, otherwise a user decides a scheduler in the model-stepping function, as illustrated in the Advanced stepping part of the tutorial.


A non-allocating scheduler that activates all agents agents at each step according to their id.


A non-allocating scheduler that at each step activates the agents in an order dictated by their property, with agents with greater property acting first. property can be a Symbol, which just dictates which field of the agents to compare, or a function which inputs an agent and outputs a real number.

Schedulers.ByType(shuffle_types::Bool, shuffle_agents::Bool, agent_union)

A non-allocating scheduler useful only for mixed agent models using Union types.

  • Setting shuffle_types = true groups by agent type, but randomizes the type order.

Otherwise returns agents grouped in order of appearance in the Union.

  • shuffle_agents = true randomizes the order of agents within each group, false returns

the default order of the container (equivalent to Schedulers.fastest).

  • agent_union is a Union of all valid agent types (as passed to ABM)
Schedulers.ByType((C, B, A), shuffle_agents::Bool)

A non-allocating scheduler that activates agents by type in specified order (since Unions are not order preserving). shuffle_agents = true randomizes the order of agents within each group.


A non-allocating scheduler that at each step activates only p percentage of randomly chosen agents.


A non-allocating scheduler that activates all agents once per step in a random order. Different random ordering is used at each different step.


A scheduler that activates all agents agents at each step according to their id.


A scheduler that at each step activates the agents in an order dictated by their property, with agents with greater property acting first. property can be a Symbol, which just dictates which field of the agents to compare, or a function which inputs an agent and outputs a real number.

Schedulers.by_type(shuffle_types::Bool, shuffle_agents::Bool)

A scheduler useful only for mixed agent models using Union types.

  • Setting shuffle_types = true groups by agent type, but randomizes the type order.

Otherwise returns agents grouped in order of appearance in the Union.

  • shuffle_agents = true randomizes the order of agents within each group, false returns

the default order of the container (equivalent to Schedulers.fastest).

Schedulers.by_type((C, B, A), shuffle_agents::Bool)

A scheduler that activates agents by type in specified order (since Unions are not order preserving). shuffle_agents = true randomizes the order of agents within each group.


A scheduler that activates all agents once per step in the order dictated by the agent's container, which is arbitrary (the keys sequence of a dictionary). This is the fastest way to activate all agents once per step.


A scheduler that at each step activates only p percentage of randomly chosen agents.


A scheduler that activates all agents once per step in a random order. Different random ordering is used at each different step.


Submodule for functionality related to OpenStreetMapSpace. See the docstring of the space for more info.


This struct stores information about the path of an agent via route planning. It also serves as developer's docs for some of the internals of OpenStreetMapSpace.

Storage of map nodes

Each node has a node ID from the OpenStreetMap API. The map is stored as a Graph by LightOSM, and hence each node also has a vertex index corresponding to the vertex representing it in this graph. Hence each node can be referred to by either the node ID or its graph index, and we can convert either way, using the function LightOSM.index_to_node. We use graph vertex indices consistently in OSMSpace, because we access graph data more often than OSM data.

Fields of OpenStreetMapPath

  • route::Vector{Int}: Vertex indices along the planned route. They are actually stored in inverse sequence, from dest to start, because it is more efficient to pop the off this way while traversing the route.
  • start::Tuple{Int,Int,Float64}: Initial position of the agent.
  • dest::Tuple{Int,Int,Float64}: Destination.
  • return_route::Vector{Int}: Same as route but for the return trip.
  • has_to_return::Bool: true if there is an actual return trip.
OpenStreetMapSpace(path::AbstractString; kwargs...)

Create a space residing on the Open Street Map (OSM) file provided via path. This space represents the underlying map as a continuous entity choosing accuracy over performance. The map is represented as a graph, consisting of nodes connected by edges. Nodes are not necessarily intersections, and there may be multiple nodes on a road joining two intersections. Agents move along the available roads of the map using routing, see below.

The functionality related to Open Street Map spaces is in the submodule OSM. An example of its usage can be found in Zombie Outbreak in a City.

The OSMAgent

The base properties for an agent residing on an OSMSpace are as follows:

mutable struct Agent <: AbstractAgent

Current position tuple is represented as (first intersection index, second intersection index, distance travelled). The indices are the indices of the nodes of the graph that internally represents the map. Functions like OSM.nearest_node or OSM.nearest_road can help find those node indices from a (lon, lat) real world coordinate. The distance travelled is in the units of weight_type. This ensures that the map is a continuous kind of space, as an agent can truly be at any possible point on an existing road.

Use OSMAgent for convenience.

Obtaining map files

Maps files can be downloaded using the functions provided by LightOSM.jl. Agents.jl also re-exports OSM.download_osm_network, the main function used to download maps and provides a test map in OSM.test_map. An example usage to download the map of London to "london.json":

    place_name = "London",
    save_to_file_location = "london.json"

The length of an edge between two nodes is specified in the units of the map's weight_type as listed in the documentation for LightOSM.OSMGraph. The possible weight_types are:

  • :distance: The distance in kilometers of an edge
  • :time: The time in hours to travel along an edge at the maximum speed allowed on that road
  • :lane_efficiency: Time scaled by number of lanes

The default weight_type used is :distance.

All kwargs are propagated to LightOSM.graph_from_file.

Routing with OSM

You can use plan_route! or plan_random_route!. To actually move along a planned route use move_along_route!.

OSM.closest_node_on_edge(a::Tuple{Int,Int,Float64}, model::ABM{<:OpenStreetMapSpace})

Return the node that the given point is closest to on its edge

OSM.distance(pos_1, pos_2, model::ABM{<:OpenStreetMapSpace}; kwargs...)

Return the distance between the two positions along the shortest path joining them in the given model. Return Inf if no such path exists.

All keywords are passed to LightOSM.shortest_path.

OSM.get_geoloc(pos::Int, model::ABM{<:OpenStreetMapSpace})

Return GeoLocation corresponding to node pos.

OSM.get_reverse_direction(pos::Tuple{Int,Int,Float64}, model::ABM{<:OpenStreetMapSpace})

Return the same position, but with pos[1] and pos[2] swapped and pos[3] updated.

OSM.lonlat(pos, model)
OSM.lonlat(agent, model)

Return (longitude, latitude) of current road or intersection position.

OSM.nearest_node(lonlat::Tuple{Float64,Float64}, model::ABM{<:OpenStreetMapSpace})

Return the nearest intersection position to (longitude, latitude). Quicker, but less precise than OSM.nearest_road.

OSM.nearest_road(lonlat::Tuple{Float64,Float64}, model::ABM{<:OpenStreetMapSpace})

Return a location on a road nearest to (longitude, latitude). Slower, but more precise than OSM.nearest_node.

OSM.plan_random_route!(agent, model::ABM{<:OpenStreetMapSpace}; kwargs...) → success

Plan a new random route for the agent, by selecting a random destination and planning a route from the agent's current position. Overwrite any existing route.

The keyword limit = 10 specifies the limit on the number of attempts at planning a random route, as no connection may be possible given the random destination. Return true if a route was successfully planned, false otherwise. All other keywords are passed to plan_route!


Similar to random_position, but rather than providing only intersections, this method returns a location somewhere on a road heading in a random direction.

OSM.road_length(start::Int, finish::Int, model)
OSM.road_length(pos::Tuple{Int,Int,Float64}, model)

Return the road length between two intersections. This takes into account the direction of the road, so OSM.road_length(pos_1, pos_2, model) may not be the same as OSM.road_length(pos_2, pos_1, model). Units of the returned quantity are as specified by the underlying graph's weight_type. If start and finish are the same or pos[1] and pos[2] are the same, then return 0.

OSM.route_length(agent, model::ABM{<:OpenStreetMapSpace})

Return the length of the route planned for the given agent, correctly taking into account the amount of route already traversed by the agent. Return 0 if is_stationary(agent, model).

OSM.same_position(a::Tuple{Int,Int,Float64}, b::Tuple{Int,Int,Float64}, model::ABM{<:OpenStreetMapSpace})

Return true if the given positions a and b are (approximately) identical

OSM.same_road(a::Tuple{Int,Int,Float64}, b::Tuple{Int,Int,Float64})

Return true if both points lie on the same road of the graph


Download a small test map of Göttingen as an artifact. Return a path to the downloaded file.

Using this map requires network_type = :none to be passed as a keyword to OSMSpace. The unit of distance used for this map is :time.

move_along_route!(agent, model::ABM{<:OpenStreetMapSpace}, distance::Real) → remaining

Move an agent by distance along its planned route. Units of distance are as specified by the underlying graph's weight_type. If the provided distance is greater than the distance to the end of the route, return the remaining distance. Otherwise, return 0. 0 is also returned if is_stationary(agent, model).

plan_route!(agent, dest, model::ABM{<:OpenStreetMapSpace};
            return_trip = false, kwargs...) → success

Plan a route from the current position of agent to the location specified in dest, which can be an intersection or a point on a road. Overwrite any existing route.

If return_trip = true, a route will be planned from start ⟶ finish ⟶ start. All other keywords are passed to LightOSM.shortest_path.

Return true if a path to dest exists, and hence the route planning was successful. Otherwise return false. Specifying return_trip = true also requires the existence of a return path for a route to be planned.



CI codecov Package Downloads

Agents.jl is a pure Julia framework for agent-based modeling (ABM): a computational simulation methodology where autonomous agents react to their environment (including other agents) given a predefined set of rules. Some major highlights of Agents.jl are:

  1. It is fast (faster than MASON, NetLogo, or Mesa)
  2. It is simple: has a very short learning curve and requires writing minimal code
  3. Has an extensive interface of thousands of out-of-the box possible agent actions
  4. Straightforwardly allows simulations on Open Street Maps

The simplicity of Agents.jl is due to the intuitive space-agnostic modelling approach we have implemented: agent actions are specified using generically named functions (such as "move agent" or "find nearby agents") that do not depend on the actual space the agents exist in, nor on the properties of the agents themselves. Overall this leads to ultra fast model prototyping where even changing the space the agents live in is matter of only a couple of lines of code.

More information and an extensive list of features can be found in the documentation, which you can either find online or build locally by running the docs/make.jl file.


If you use this package in a publication, or simply want to refer to it, please cite the paper below:

  doi = {10.1177/00375497211068820},
  url = {},
  year = {2022},
  month = jan,
  publisher = {{SAGE} Publications},
  pages = {003754972110688},
  author = {George Datseris and Ali R. Vahdati and Timothy C. DuBois},
  title = {Agents.jl: a performant and feature-full agent-based modeling software of minimal code complexity},
  journal = {{SIMULATION}},
  volume = {0},
  number = {0},
ABMObservable(model; agent_step!, model_step!, adata, mdata, when) → abmobs

abmobs contains all information necessary to step an agent based model interactively. It is also returned by abmplot.

Calling Agents.step!(abmobs, n) will step the model for n using the provided agent_step!, model_step!, n as in Agents.step!.

The fields abmobs.model, abmobs.adf, abmobs.mdf are observables that contain the AgentBasedModel, and the agent and model dataframes with collected data. Data are collected as described in! using the adata, mdata, when keywords. All three observables are updated on stepping (when it makes sense). The field abmobs.s is also an observable containing the current step number.

All plotting and interactivity should be defined by lifting these observables.

YourAgentType <: AbstractAgent

Agents participating in Agents.jl simulations are instances of user-defined Types that are subtypes of AbstractAgent.

Your agent type(s) must have the id::Int field as first field. If any space is used (see Available spaces), a pos field of appropriate type is also mandatory. The core model structure, and each space, may also require additional fields that may, or may not, be communicated as part of the public API.

The @agent macro ensures that all of these constrains are in place and hence it is the recommended way to generate new agent types.


Abstract type for grid-based spaces. All instances have a field stored_ids which is simply the array whose size is the same as the size of the space and whose cartesian indices are the possible positions in the space.

Furthermore, all spaces should have at least the fields

  • offsets_within_radius
  • offsets_within_radius_no_0

which are Dict{Float64,Vector{NTuple{D,Int}}}, mapping radii to vector of indices within each radius.

D is the dimension and P is whether the space is periodic (boolean).


Supertype of all concrete space implementations for Agents.jl.


An AgentBasedModel is the supertype encompassing models in Agents.jl. All models are some concrete implementation of AgentBasedModel and follow its interface (see below). ABM is an alias to AgentBasedModel.

A model is typically constructed with:

AgentBasedModel(AgentType [, space]; properties, kwargs...) → model

which creates a model expecting agents of type AgentType living in the given space. AgentBasedModel(...) defaults to StandardABM, which stores agents in a dictionary that maps unique IDs (integers) to agents. See also UnremovableABM for better performance in case number of agents can only increase during the model evolution.

Agents.jl supports multiple agent types by passing a Union of agent types as AgentType. However, please have a look at Performance Tips for potential drawbacks of this approach.

space is a subtype of AbstractSpace, see Space for all available spaces. If it is omitted then all agents are virtually in one position and there is no spatial structure. Spaces are mutable objects and are not designed to be shared between models. Create a fresh instance of a space with the same properties if you need to do this.


  • properties = nothing: additional model-level properties that the user may decide upon and include in the model. properties can be an arbitrary container of data, however it is most typically a Dict with Symbol keys, or a composite type (struct).
  • scheduler = Schedulers.fastest: is the scheduler that decides the (default) activation order of the agents. See the scheduler API for more options.
  • rng = Random.default_rng(): the random number generation stored and used by the model in all calls to random functions. Accepts any subtype of AbstractRNG.
  • warn=true: some type tests for AgentType are done, and by default warnings are thrown when appropriate.

Interface of AgentBasedModel

Here we the most important information on how to query an instance of AgentBasedModel:

  • model[id] gives the agent with given id.
  • abmproperties(model) gives the properies container stored in the model.
  • If the model properties is a dictionary with key type Symbol, or if it is a composite type (struct), then the syntax will return the model property with key :property.
  • abmrng(model) will return the random number generator of the model. It is strongly recommended to use abmrng(model) to all calls to rand and similar functions, so that reproducibility can be established in your modelling workflow.
  • abmscheduler(model) will return the default scheduler of the model.

Many more functions exist in the API page, such as allagents.

Arccos(a, b)

Create a ContinuousUnivariateDistribution corresponding to acos(Uniform(a,b)).

ContinuousAgent{D} <: AbstractAgent

The minimal agent struct for usage with D-dimensional ContinuousSpace. It has the additional fields pos::NTuple{D,Float64}, vel::NTuple{D,Float64}. See also @agent.

ContinuousSpace(extent::NTuple{D, <:Real}; kwargs...)

Create a D-dimensional ContinuousSpace in range 0 to (but not including) extent. Your agent positions (field pos) must be of type NTuple{D, <:Real}, and it is strongly recommend that agents also have a field vel::NTuple{D, <:Real} to use in conjunction with move_agent!. Use ContinuousAgent for convenience.

ContinuousSpace is a representation of agent dynamics on a continuous medium where agent position, orientation, and speed, are true floats. In addition, support is provided for representing spatial properties in a model that contains a ContinuousSpace. Spatial properties (which typically are contained in the model properties) can either be functions of the position vector, f(pos) = value, or AbstractArrays, representing discretizations of spatial data that may not be available in analytic form. In the latter case, the position is automatically mapped into the discretization represented by the array. Use get_spatial_property to access spatial properties in conjunction with ContinuousSpace.

See also Continuous space exclusives on the online docs for more functionality. An example using continuous space is the Flocking model.

Distance specification

Distances specified by r in functions like nearby_ids are always based on the Euclidean distance between two points in ContinuousSpace.

In ContinuousSpace nearby_* searches are accelerated using a grid system, see discussion around the keyword spacing below. nearby_ids is not an exact search, but can be a possible over-estimation, including agent IDs whose distance slightly exceeds r with "slightly" being as much as spacing. If you want exact searches use the slower nearby_ids_exact.


  • periodic = true: Whether the space is periodic or not. If set to false an error will occur if an agent's position exceeds the boundary.
  • spacing::Real = minimum(extent)/20: Configures an internal compartment spacing that is used to accelerate nearest neighbor searches like nearby_ids. The compartments are actually a full instance of GridSpace in which agents move. All dimensions in extent must be completely divisible by spacing. There is no best choice for the value of spacing and if you need optimal performance it's advised to set up a benchmark over a range of choices. The finer the spacing, the faster and more accurate the inexact version of nearby_ids becomes. However, a finer spacing also means slower move_agent!, as agents change compartments more often.
  • update_vel!: A function, update_vel!(agent, model) that updates the agent's velocity before the agent has been moved, see move_agent!. You can of course change the agents' velocities during the agent interaction, the update_vel! functionality targets spatial force fields acting on the agents individually (e.g. some magnetic field). If you use update_vel!, the agent type must have a field vel::NTuple{D, <:Real}.
FixedMassABM(agent_vector [, space]; properties, kwargs...) → model

Similar to UnremovableABM, but agents cannot be removed nor added. Hence, all agents in the model must be provided in advance as a vector. This allows storing agents into a SizedVector, a special vector with statically typed size which is the same as the size of the input agent_vector. This version of agent based model offers better performance than UnremovableABM if the number of agents is important and used often in the simulation.

It is mandatory that the agent ID is exactly the same as its position in the given agent_vector.


Create a GraphSpace instance that is underlined by an arbitrary graph from Graphs.jl. GraphSpace represents a space where each node (i.e. position) of a graph can hold an arbitrary amount of agents, and each agent can move between the nodes of the graph. The position type for this space is Int, use GraphAgent for convenience.

Graphs.nv and can be used in a model with a GraphSpace to obtain the number of nodes or edges in the graph. The underlying graph can be altered using add_vertex! and rem_vertex!.

An example using GraphSpace is SIR model for the spread of COVID-19.

If you want to model social networks, where each agent is equivalent with a node of a graph, you're better of using nothing as the model space, and using a graph from Graphs.jl directly in the model parameters, as shown in the Social networks with Graphs.jl integration example.

Distance specification

In functions like nearby_ids, distance for GraphSpace means the degree of neighbors in the graph (thus distance is always an integer). For example, for r=2 includes first and second degree neighbors. For 0 distance, the search occurs only on the origin node.

In functions like nearby_ids the keyword neighbor_type=:default can be used to select differing neighbors depending on the underlying graph directionality type.

  • :default returns neighbors of a vertex (position). If graph is directed, this is equivalent to :out. For undirected graphs, all options are equivalent to :out.
  • :all returns both :in and :out neighbors.
  • :in returns incoming vertex neighbors.
  • :out returns outgoing vertex neighbors.
GridAgent{D} <: AbstractAgent

The minimal agent struct for usage with D-dimensional GridSpace. It has an additional pos::NTuple{D,Int} field. See also @agent.

GridSpace(d::NTuple{D, Int}; periodic = true, metric = :chebyshev)

Create a GridSpace that has size given by the tuple d, having D ≥ 1 dimensions. Optionally decide whether the space will be periodic and what will be the distance metric. The position type for this space is NTuple{D, Int}, use GridAgent for convenience. Valid positions have indices in the range 1:d[i] for the i-th dimension.

An example using GridSpace is Schelling's segregation model.

Distance specification

The typical terminology when searching neighbors in agent based modelling is "Von Neumann" neighborhood or "Moore" neighborhoods. However, because Agents.jl provides a much more powerful infrastructure for finding neighbors, both in arbitrary dimensions but also of arbitrary neighborhood size, this established terminology is no longer appropriate. Instead, distances that define neighborhoods are specified according to a proper metric space, that is both well defined for any distance, and applicable to any dimensionality.

The allowed metrics are (and see docs online for a plotted example):

  • :chebyshev metric means that the r-neighborhood of a position are all positions within the hypercube having side length of 2*floor(r) and being centered in the origin position. This is similar to "Moore" for r = 1 and two dimensions.

  • :manhattan metric means that the r-neighborhood of a position are all positions whose cartesian indices have Manhattan distance ≤ r from the cartesian index of the origin position. This similar to "Von Neumann" for r = 1 and two dimensions.

  • :euclidean metric means that the r-neighborhood of a position are all positions whose cartesian indices have Euclidean distance ≤ r from the cartesian index of the origin position.

Advanced dimension-dependent distances in Chebyshev metric

If metric = :chebyshev, some advanced specification of distances is allowed when providing r to functions like nearby_ids.

  1. r::NTuple{D,Int} such as r = (5, 2). This would mean a distance of 5 in the first dimension and 2 in the second. This can be useful when different coordinates in the space need to be searched with different ranges, e.g., if the space corresponds to a full building, with the third dimension the floor number.
  2. r::Vector{Tuple{Int,UnitRange{Int}}} such as r = [(1, -1:1), (3, 1:2)]. This allows explicitly specifying the difference between position indices in each specified dimension. The example r = [(1, -1:1), (3, 1:2)] when given to e.g., nearby_ids, would search dimension 1 one step of either side of the current position (as well as the current position since 0 ∈ -1:1) and would search the third dimension one and two positions above current. Unspecified dimensions (like the second in this example) are searched throughout all their possible ranges.

See the Battle Royale example for usage of this advanced specification of dimension-dependent distances where one dimension is used as a categorical one.

GridSpaceSingle(d::NTuple{D, Int}; periodic = true, metric = :chebyshev)

This is a specialized version of GridSpace that allows only one agent per position, and utilizes this knowledge to offer significant performance gains versus GridSpace.

This space reserves agent ID = 0 for internal usage. Agents should be initialized with non-zero IDs, either positive or negative. This is not checked internally.

All arguments and keywords behave exactly as in GridSpace.

NoSpaceAgent <: AbstractAgent

The minimal agent struct for usage with nothing as space (i.e., no space). It has the field id::Int, and potentially other internal fields that are not documented as part of the public API. See also @agent.

StandardABM(AgentType [, space]; properties, kwargs...) → model

The most standard concrete implementation of an AgentBasedModel, as well as the default version of the generic AgentBasedModel constructor. StandardABM stores agents in a dictionary mapping unique Int IDs to agents. See also UnremovableABM.

UnremovableABM(AgentType [, space]; properties, kwargs...) → model

Similar to StandardABM, but agents cannot be removed, only added. This allows storing agents more efficiently in a standard Julia Vector (as opposed to the Dict used by StandardABM, yielding faster retrieval and iteration over agents.

It is mandatory that the agent ID is exactly the same as the agent insertion order (i.e., the 5th agent added to the model must have ID 5). If not, an error will be thrown by add_agent!.

abmexploration(model::ABM; alabels, mlabels, kwargs...)

Open an interactive application for exploring an agent based model and the impact of changing parameters on the time evolution. Requires Agents.

The application evolves an ABM interactively and plots its evolution, while allowing changing any of the model parameters interactively and also showing the evolution of collected data over time (if any are asked for, see below). The agent based model is plotted and animated exactly as in abmplot, and the model argument as well as splatted kwargs are propagated there as-is. This convencience function only works for aggregated agent data.

Calling abmexploration returns: fig::Figure, abmobs::ABMObservable. So you can save and/or further modify the figure and it is also possible to access the collected data (if any) via the ABMObservable.

Clicking the "reset" button will add a red vertical line to the data plots for visual guidance.

Keywords arguments (in addition to those in abmplot)

  • alabels, mlabels: If data are collected from agents or the model with adata, mdata, the corresponding plots' y-labels are automatically named after the collected data. It is also possible to provide alabels, mlabels (vectors of strings with exactly same length as adata, mdata), and these labels will be used instead.
  • figure = NamedTuple(): Keywords to customize the created Figure.
  • axis = NamedTuple(): Keywords to customize the created Axis.
  • plotkwargs = NamedTuple(): Keywords to customize the styling of the resulting scatterlines plots.
abmplot(model::ABM; kwargs...) → fig, ax, abmobs
abmplot!(ax::Axis/Axis3, model::ABM; kwargs...) → abmobs

Plot an agent based model by plotting each individual agent as a marker and using the agent's position field as its location on the plot. The same function is used to make custom composite plots and animations for the model evolution using the returned abmobs. abmplot is also used to launch interactive GUIs for evolving agent based models, see "Interactivity" below.

See also abmvideo and abmexploration.

Keyword arguments

Agent related

  • ac, as, am : These three keywords decide the color, size, and marker, that each agent will be plotted as. They can each be either a constant or a function, which takes as an input a single agent and outputs the corresponding value. If the model uses a GraphSpace, ac, as, am functions instead take an iterable of agents in each position (i.e. node of the graph).

    Using constants: ac = "#338c54", as = 15, am = :diamond

    Using functions:

    ac(a) = a.status == :S ? "#2b2b33" : a.status == :I ? "#bf2642" : "#338c54"
    as(a) = 10rand()
    am(a) = a.status == :S ? :circle : a.status == :I ? :diamond : :rect

    Notice that for 2D models, am can be/return a Makie.Polygon instance, which plots each agent as an arbitrary polygon. It is assumed that the origin (0, 0) is the agent's position when creating the polygon. In this case, the keyword as is meaningless, as each polygon has its own size. Use the functions scale, rotate_polygon to transform this polygon.

    3D models currently do not support having different markers. As a result, am cannot be a function. It should be a Mesh or 3D primitive (such as Sphere or Rect3D).

  • offset = nothing : If not nothing, it must be a function taking as an input an agent and outputting an offset position tuple to be added to the agent's position (which matters only if there is overlap).

  • scatterkwargs = () : Additional keyword arguments propagated to the scatter! call.

Preplot related

  • heatarray = nothing : A keyword that plots a model property (that is a matrix) as a heatmap over the space. Its values can be standard data accessors given to functions like run!, i.e. either a symbol (directly obtain model property) or a function of the model. If the space is AbstractGridSpace then matrix must be the same size as the underlying space. For ContinuousSpace any size works and will be plotted over the space extent. For example heatarray = :temperature is used in the Daisyworld example. But you could also define f(model) = create_matrix_from_model... and set heatarray = f. The heatmap will be updated automatically during model evolution in videos and interactive applications.
  • heatkwargs = NamedTuple() : Keywords given to Makie.heatmap function if heatarray is not nothing.
  • add_colorbar = true : Whether or not a Colorbar should be added to the right side of the heatmap if heatarray is not nothing. It is strongly recommended to use abmplot instead of the abmplot! method if you use heatarray, so that a colorbar can be placed naturally.
  • static_preplot! : A function f(ax, model) that plots something after the heatmap but before the agents.
  • osmkwargs = NamedTuple() : keywords directly passed to OSMMakie.osmplot! if model space is OpenStreetMapSpace.
  • graphplotkwargs = NamedTuple() : keywords directly passed to GraphMakie.graphplot! if model space is GraphSpace.
  • adjust_aspect = true: Adjust axis aspect ratio to be the model's space aspect ratio.

The stand-alone function abmplot also takes two optional NamedTuples named figure and axis which can be used to change the automatically created Figure and Axis objects.


Evolution related

  • agent_step!, model_step! = Agents.dummystep: Stepping functions to pass to ABMObservable which itself passes to Agents.step!.
  • add_controls::Bool: If true, abmplot switches to "interactive application" mode. This is by default true if either agent_step! or model_step! keywords are provided. These stepping functions are used to evolve the model interactively using Agents.step!. The application has the following interactive elements:
    1. "step": advances the simulation once for spu steps.
    2. "run": starts/stops the continuous evolution of the model.
    3. "reset model": resets the model to its initial state from right after starting the interactive application.
    4. Two sliders control the animation speed: "spu" decides how many model steps should be done before the plot is updated, and "sleep" the sleep() time between updates.
  • enable_inspection = add_controls: If true, enables agent inspection on mouse hover.
  • spu = 1:50: The values of the "spu" slider.
  • params = Dict() : This is a dictionary which decides which parameters of the model will be configurable from the interactive application. Each entry of params is a pair of Symbol to an AbstractVector, and provides a range of possible values for the parameter named after the given symbol (see example online). Changing a value in the parameter slides is only propagated to the actual model after a press of the "update" button.

Data collection related

  • adata, mdata, when: Same as the keyword arguments of!. If either or both adata, mdata are given, data are collected and stored in the abmobs, see ABMObservable. The same keywords provide the data plots of abmexploration. This also adds the button "clear data" which deletes previously collected agent and model data by emptying the underlying DataFrames adf/mdf. Reset model and clear data are independent processes.

See the documentation string of ABMObservable for custom interactive plots.

abmplot!(ax::Axis, model::ABM; kwargs...)

See abmplot.


Return the properties container stored in the model.


Return the random number generator stored in the model.


Return the space instance stored in the model.

abmvideo(file, model, agent_step! [, model_step!]; kwargs...)

This function exports the animated time evolution of an agent based model into a video saved at given path file, by recording the behavior of the interactive version of abmplot (without sliders). The plotting is identical as in abmplot and applicable keywords are propagated.


  • spf = 1: Steps-per-frame, i.e. how many times to step the model before recording a new frame.
  • framerate = 30: The frame rate of the exported video.
  • frames = 300: How many frames to record in total, including the starting frame.
  • title = "": The title of the figure.
  • showstep = true: If current step should be shown in title.
  • figure = NamedTuple(): Figure related keywords (e.g. resolution, backgroundcolor).
  • axis = NamedTuple(): Axis related keywords (e.g. aspect).
  • recordkwargs = NamedTuple(): Keyword arguments given to Makie.record. You can use (compression = 1, profile = "high") for a higher quality output, and prefer the CairoMakie backend. (compression 0 results in videos that are not playable by some software)
  • kwargs...: All other keywords are propagated to abmplot.
add_agent!(agent::AbstractAgent [, pos], model::ABM) → agent

Add the agent to the model in the given position. If pos is not given, the agent is added to a random position. The agent's position is always updated to match position, and therefore for add_agent! the position of the agent is meaningless. Use add_agent_pos! to use the agent's position.

The type of pos must match the underlying space position type.

add_agent!([pos,] model::ABM, args...; kwargs...) → newagent

Create and add a new agent to the model using the constructor of the agent type of the model. Optionally provide a position to add the agent to as first argument, which must match the space position type.

This function takes care of setting the agent's id and position. The extra provided args... and kwargs... are propagated to other fields of the agent constructor (see example below).

add_agent!([pos,] A::Type, model::ABM, args...; kwargs...) → newagent

Use this version for mixed agent models, with A the agent type you wish to create (to be called as A(id, pos, args...; kwargs...)), because it is otherwise not possible to deduce a constructor for A.


using Agents
mutable struct Agent <: AbstractAgent
Agent(id, pos; w=0.5, k=false) = Agent(id, pos, w, k) # keyword constructor
model = ABM(Agent, GraphSpace(complete_digraph(5)))

add_agent!(model, 1, 0.5, true) # incorrect: id/pos is set internally
add_agent!(model, 0.5, true) # correct: w becomes 0.5
add_agent!(5, model, 0.5, true) # add at position 5, w becomes 0.5
add_agent!(model; w = 0.5) # use keywords: w becomes 0.5, k becomes false
add_agent_pos!(agent::AbstractAgent, model::ABM) → agent

Add the agent to the model at the agent's own position.

add_agent_single!(agent, model::ABM{<:DiscreteSpace}) → agent

Add the agent to a random position in the space while respecting a maximum of one agent per position, updating the agent's position to the new one.

This function does nothing if there aren't any empty positions.

add_agent_single!(model::ABM{<:DiscreteSpace}, properties...; kwargs...)

Same as add_agent!(model, properties...; kwargs...) but ensures that it adds an agent into a position with no other agents (does nothing if no such position exists).

add_agent_to_model!(agent, model)

Add the agent to the model's internal container, if the addition is valid given the agent's ID and those already in the model. Otherwise error.

add_agent_to_space!(agent, model)

Add the agent to the underlying space structure at the agent's own position. This function is called after the agent is already inserted into the model dictionary and maxid has been updated. This function is NOT part of the public API.


addnode!(model::ABM{<: GraphSpace}) Add a new node (i.e. possible position) to the model's graph and return it. You can connect this new node with existing ones using [`addedge!`](@ref).


Convert agent data into a string which is used to display all agent variables and their values in the tooltip on mouse hover. Concatenates strings if there are multiple agents at one position. Custom tooltips for agents can be implemented by adding a specialised method for agent2string.


function Agents.agent2string(agent::SpecialAgent)
    ✨ SpecialAgent ✨
    ID = $(
    Main weapon = $(agent.charisma)
    Side weapon = $(agent.pistol)
agent_validator(AgentType, space)

Validate the user supplied agent (subtype of AbstractAgent). Checks for mutability and existence and correct types for fields depending on SpaceType.

agents_in_position(position, model::ABM{<:DiscreteSpace})
agents_in_position(agent, model::ABM{<:DiscreteSpace})

Return an iterable of the agents in position, or in the position ofagent`.


Return an iterator over all agents of the model.


Return an iterator over all agent IDs of the model.


given position in continuous space, return continuous space coordinates of cell center.

collect_agent_data!(df, model, properties, step = 0; obtainer = identity)

Collect and add agent data into df (see run! for the dispatch rules of properties and obtainer). step is given because the step number information is not known.

dataname(k) → name

Return the name of the column of the i-th collected data where k = adata[i] (or mdata[i]). dataname also accepts tuples with aggregate and conditional values.

do_checks(agent, space)

Helper function for agent_validator.

dummystep(agent, model)

Use instead of agent_step! in step! if no function is useful to be defined.


Use instead of model_step! in step! if no function is useful to be defined.

elastic_collision!(a, b, f = nothing) → happened

Resolve a (hypothetical) elastic collision between the two agents a, b. They are assumed to be disks of equal size touching tangentially. Their velocities (field vel) are adjusted for an elastic collision happening between them. This function works only for two dimensions. Notice that collision only happens if both disks face each other, to avoid collision-after-collision.

If f is a Symbol, then the agent property f, e.g. :mass, is taken as a mass to weight the two agents for the collision. By default no weighting happens.

One of the two agents can have infinite "mass", and then acts as an immovable object that specularly reflects the other agent. In this case momentum is not conserved, but kinetic energy is still conserved.

Return a boolean encoding whether the collision happened.

Example usage in Continuous space social distancing.

empty_nearby_positions(position, model::ABM{<:DiscreteSpace}, r = 1; kwargs...)
empty_nearby_positions(agent, model::ABM{<:DiscreteSpace}, r = 1; kwargs...)

Return an iterable of all empty positions within "radius" r of the given position or the position of an agent::AbstractAgent.

The value of r and possible keywords operate identically to nearby_positions.

This function only exists for discrete spaces with a finite amount of positions.


Return a list of positions that currently have no agents on them.

ensemblerun!(models::Vector, agent_step!, model_step!, n; kwargs...)

Perform an ensemble simulation of run! for all model ∈ models. Each model should be a (different) instance of an AgentBasedModel but probably initialized with a different random seed or different initial agent distribution. All models obey the same rules agent_step!, model_step! and are evolved for n.

Similarly to run! this function will collect data. It will furthermore add one additional column to the dataframe called :ensemble, which has an integer value counting the ensemble member. The function returns agent_df, model_df, models.

If you want to scan parameters and at the same time run multiple simulations at each parameter combination, simply use seed as a parameter, and use that parameter to tune the model's initial random seed and/or agent distribution.

See example usage in Schelling's segregation model.


The following keywords modify the ensemblerun! function:

  • parallel::Bool = false whether Distributed.pmap is invoked to run simulations in parallel. This must be used in conjunction with @everywhere (see Performance Tips).
  • showprogress::Bool = false whether a progressbar will be displayed to indicate % runs finished.

All other keywords are propagated to run! as-is.

ensemblerun!(generator, agent_step!, model_step!, n; kwargs...)

Generate many ABMs and propagate them into ensemblerun!(models, ...) using the provided generator which is a one-argument function whose input is a seed.

This method has additional keywords ensemble = 5, seeds = rand(UInt32, ensemble).

euclidean_distance(a, b, model::ABM)

Return the euclidean distance between a and b (either agents or agent positions), respecting periodic boundary conditions (if in use). Works with any space where it makes sense: currently AbstractGridSpace and ContinuousSpace.

Example usage in the Flocking model.

fill_space!([A ,] model::ABM{<:DiscreteSpace,A}, args...; kwargs...)
fill_space!([A ,] model::ABM{<:DiscreteSpace,A}, f::Function; kwargs...)

Add one agent to each position in the model's space. Similarly with add_agent!, the function creates the necessary agents and the args...; kwargs... are propagated into agent creation. If instead of args... a function f is provided, then args = f(pos) is the result of applying f where pos is each position (tuple for grid, integer index for graph).

An optional first argument is an agent type to be created, and targets mixed agent models where the agent constructor cannot be deduced (since it is a union).

get_direction(from, to, model::ABM)

Return the direction vector from the position from to position to taking into account periodicity of the space.

get_spatial_index(pos, property::AbstractArray, model::ABM)

Convert the continuous agent position into an appropriate index of property, which represents some discretization of a spatial field over a ContinuousSpace.

The dimensionality of property and the continuous space do not have to match. If property has lower dimensionality than the space (e.g. representing some surface property in 3D space) then the front dimensions of pos will be used to index.

get_spatial_property(pos::NTuple{D, Float64}, property::AbstractArray, model::ABM)

Convert the continuous agent position into an appropriate index of property, which represents some discretization of a spatial field over a ContinuousSpace. Then, return property[index]. To get the index directly, for e.g. mutating the property in-place, use get_spatial_index.

get_spatial_property(pos::NTuple{D, Float64}, property::Function, model::ABM)

Literally equivalent with property(pos, model), provided just for syntax consistency.


Return a function to write to file using a given backend. The returned writer function will take three arguments: filename, data to write, whether to append to existing file or not.


Return true if there are any positions in the model without agents.

id_in_position(pos, model::ABM{<:GridSpaceSingle}) → id

Return the agent ID in the given position. This will be 0 if there is no agent in this position.

This is similar to ids_in_position, but specialized for GridSpaceSingle. See also isempty.

ids_in_position(position, model::ABM{<:DiscreteSpace})
ids_in_position(agent, model::ABM{<:DiscreteSpace})

Return the ids of agents in the position corresponding to position or position of agent.

index_mapped_groups(order::Int, model::ABM; scheduler = Schedulers.by_id)
index_mapped_groups(order::Int, model::ABM, filter::Function; scheduler = Schedulers.by_id)

Return an iterable of agent ids in the model, meeting the filter criteria if used.

interacting_pairs(model, r, method; scheduler = model.scheduler) → piter

Return an iterator that yields unique pairs of agents (a, b) that are close neighbors to each other, within some interaction radius r.

This function is usefully combined with model_step!, when one wants to perform some pairwise interaction across all pairs of close agents once (and does not want to trigger the event twice, both with a and with b, which would be unavoidable when using agent_step!). This means, that if a pair (a, b) exists, the pair (b, a) is not included in the iterator!

Use piter.pairs to get a vector of pair IDs from the iterator.

The argument method provides three pairing scenarios

  • :all: return every pair of agents that are within radius r of each other, not only the nearest ones.
  • :nearest: agents are only paired with their true nearest neighbor (existing within radius r). Each agent can only belong to one pair, therefore if two agents share the same nearest neighbor only one of them (sorted by distance, then by next id in scheduler) will be paired.
  • :types: For mixed agent models only. Return every pair of agents within radius r (similar to :all), only capturing pairs of differing types. For example, a model of Union{Sheep,Wolf} will only return pairs of (Sheep, Wolf). In the case of multiple agent types, e.g. Union{Sheep, Wolf, Grass}, skipping pairings that involve Grass, can be achieved by a scheduler that doesn't schedule Grass types, i.e.: scheduler(model) = ( for a in allagents(model) if !(a isa Grass)).

The following keywords can be used:

  • scheduler = model.scheduler, which schedulers the agents during iteration for finding pairs. Especially in the :nearest case, this is important, as different sequencing for the agents may give different results (if b is the nearest agent for a, but a is not the nearest agent for b, whether you get the pair (a, b) or not depends on whether a was scheduler first or not).
  • nearby_f = nearby_ids_exact is the function that decides how to find nearby IDs in the :all, :types cases. Must be nearby_ids_exact or nearby_ids.

Example usage in

Better performance with CellListMap.jl

Notice that in most applications that interacting_pairs is useful, there is significant (10x-100x) performance gain to be made by integrating with CellListMap.jl. Checkout the Integrating Agents.jl with CellListMap.jl integration example for how to do this.

is_stationary(agent, model)

Return true if agent has reached the end of its route, or no route has been set for it. Used in setups where using move_along_route! is valid.

iter_agent_groups(order::Int, model::ABM; scheduler = Schedulers.by_id)

Return an iterator over all agents of the model, grouped by order. When order = 2, the iterator returns agent pairs, e.g (agent1, agent2) and when order = 3: agent triples, e.g. (agent1, agent7, agent8). order must be larger than 1 but has no upper bound.

Index order is provided by the model scheduler by default, but can be altered with the scheduler keyword.

manhattan_distance(a, b, model::ABM)

Return the manhattan distance between a and b (either agents or agent positions), respecting periodic boundary conditions (if in use). Works with any space where it makes sense: currently AbstractGridSpace and ContinuousSpace.

map_agent_groups(order::Int, f::Function, model::ABM; kwargs...)
map_agent_groups(order::Int, f::Function, model::ABM, filter::Function; kwargs...)

Applies function f to all grouped agents of an iter_agent_groups iterator. kwargs are passed to the iterator method. f must take the form f(NTuple{O,AgentType}), where the dimension O is equal to order.

Optionally, a filter function that accepts an iterable and returns a Bool can be applied to remove unwanted matches from the results. Note: This option cannot keep matrix order, so should be used in conjunction with index_mapped_groups to associate agent ids with the resultant data.

move_agent!(agent::A, model::ABM{<:ContinuousSpace,A}, dt::Real)

Propagate the agent forwards one step according to its velocity, after updating the agent's velocity (if configured using update_vel!, see ContinuousSpace).

For this continuous space version of move_agent!, the "time evolution" is a trivial Euler scheme with dt the step size, i.e. the agent position is updated as agent.pos += agent.vel * dt.

Unlike move_agent!(agent, [pos,] model), this function respects the space size. For non-periodic spaces, agents will walk up to, but not reach, the space extent. For periodic spaces movement properly wraps around the extent.

move_agent!(agent [, pos], model::ABM) → agent

Move agent to the given position, or to a random one if a position is not given. pos must have the appropriate position type depending on the space type.

The agent's position is updated to match pos after the move.

move_agent_single!(agent, model::ABM{<:DiscreteSpace}; cutoff) → agent

Move agent to a random position while respecting a maximum of one agent per position. If there are no empty positions, the agent won't move.

The keyword cutoff = 0.998 is sent to random_empty.


Return the number of agents in the model.

nearby_agents(agent, model::ABM, r = 1; kwargs...) -> agent

Return an iterable of the agents near the position of the given agent.

The value of the argument r and possible keywords operate identically to nearby_ids.

nearby_ids(position, model::ABM, r = 1; kwargs...) → ids

Return an iterable over the IDs of the agents within distance r (inclusive) from the given position. The position must match type with the spatial structure of the model. The specification of what "distance" means depends on the space, hence it is explained in each space's documentation string. Keyword arguments are space-specific and also described in each space's documentation string.

nearby_ids always includes IDs with 0 distance to position.

nearby_ids(agent::AbstractAgent, model::ABM, r=1)

Same as nearby_ids(agent.pos, model, r) but the iterable excludes the given agent's id.

nearby_ids_exact(x, model, r = 1)

Return an iterator over agent IDs nearby x (a position or an agent). Only valid for ContinuousSpace models. Use instead of nearby_ids for a slower, but 100% accurate version. See ContinuousSpace for more details.

nearby_positions(position, model::ABM{<:DiscreteSpace}, r=1; kwargs...)

Return an iterable of all positions within "radius" r of the given position (which excludes given position). The position must match type with the spatial structure of the model.

The value of r and possible keywords operate identically to nearby_ids.

This function only exists for discrete spaces with a finite amount of positions.

nearby_positions(position, model::ABM{<:OpenStreetMapSpace}; kwargs...) → positions

For OpenStreetMapSpace this means "nearby intersections" and operates directly on the underlying graph of the OSM, providing the intersection nodes nearest to the given position.

nearby_positions(agent::AbstractAgent, model::ABM, r=1)

Same as nearby_positions(agent.pos, model, r).

nearest_neighbor(agent, model::ABM{<:ContinuousSpace}, r) → nearest

Return the agent that has the closest distance to given agent. Return nothing if no agent is within distance r.

nextid(model::ABM) → id

Return a valid id for creating a new agent with it.

normalize_position(pos, model::ABM{<:Union{AbstractGridSpace,ContinuousSpace}})

Return the position pos normalized for the extents of the space of the given model. For periodic spaces, this wraps the position along each dimension, while for non-periodic spaces this clamps the position to the space extent.


Return the number of positions of a model with a discrete space.

offline_run!(model, agent_step! [, model_step!], n::Integer; kwargs...)
offline_run!(model, agent_step!, model_step!, n::Function; kwargs...)

Do the same as run, but instead of collecting the whole run into an in-memory dataframe, write the output to a file after collecting data writing_interval times and empty the dataframe after each write. Useful when the amount of collected data is expected to exceed the memory available during execution.


  • backend=:csv : backend to use for writing data. Currently supported backends: :csv, :arrow
  • adata_filename="adata.$backend" : a file to write agent data on. Appends to the file if it already exists, otherwise creates the file.
  • mdata_filename="mdata.$backend": a file to write the model data on. Appends to the file if it already exists, otherwise creates the file.
  • writing_interval=1 : write to file every writing_interval times data collection is triggered. If the when keyword is not set, this corresponds to writing to file every writing_interval steps; otherwise, the data will be written every writing_interval times the when condition is satisfied (the same applies to when_model).
offsets_at_radius(model::ABM{<:AbstractGridSpace}, r::Real)

The function does two things:

  1. If a vector of indices exists in the model, it returns that.
  2. If not, it creates this vector, stores it in the model and then returns that.
offsets_within_radius(model::ABM{<:AbstractGridSpace}, r::Real)

The function does two things:

  1. If a vector of indices exists in the model, it returns that.
  2. If not, it creates this vector, stores it in the model and then returns that.
paramscan(parameters::AbstractDict, initialize; kwargs...) → adf, mdf

Perform a parameter scan of an ABM simulation output by collecting data from all parameter combinations into dataframes (one for agent data, one for model data). The dataframes columns are both the collected data (as in run!) but also the input parameter values used.

parameters is a dictionary with key type Symbol. Each entry of the dictionary maps a parameter key to the parameter values that should be scanned over (or to a single paramter value that will remain constant throughout the scans). The approach regarding parameters is as follows:

  • If the value of a specific key is a Vector, all values of the vector are expended as values for the parameter to scan over.
  • If the value of a specific key is not a Vector, it is assumed that whatever this value is, it corresponds to a single and constant parameter value and therefore it is not expanded or scanned over.

This is done so that parameter values that are inherently iterable (such as a String) are not wrongly expanded into their constitutents. (if the value of a parameter is itself a Vector, then you need to pass in a vector of vectors to scan the parameter)

The second argument initialize is a function that creates an ABM and returns it. It must accept keyword arguments which are the keys of the parameters dictionary. Since the user decides how to use input arguments to make an ABM, parameters can be used to affect model properties, space type and creation as well as agent properties, see the example below.


The following keywords modify the paramscan function:

  • include_constants::Bool = false: by default, only the varying parameters (Vector values in parameters) will be included in the output DataFrame. If true, constant parameters (non-Vector in parameters) will also be included.
  • parallel::Bool = false whether Distributed.pmap is invoked to run simulations in parallel. This must be used in conjunction with @everywhere (see Performance Tips).
  • showprogress::Bool = false whether a progressbar will be displayed to indicate % runs finished.

All other keywords are propagated into run!. Furthermore, agent_step!, model_step!, n are also keywords here, that are given to run! as arguments. Naturally, stepping functions and the number of time steps (agent_step!, model_step!, and n) and at least one of adata, mdata are mandatory. The adata, mdata lists shouldn't contain the parameters that are already in the parameters dictionary to avoid duplication.


A runnable example that uses paramscan is shown in Schelling's segregation model. There, we define

function initialize(; numagents = 320, griddims = (20, 20), min_to_be_happy = 3)
    space = GridSpaceSingle(griddims, periodic = false)
    properties = Dict(:min_to_be_happy => min_to_be_happy)
    model = ABM(SchellingAgent, space;
                properties = properties, scheduler = Schedulers.randomly)
    for n in 1:numagents
        agent = SchellingAgent(n, (1, 1), false, n < numagents / 2 ? 1 : 2)
        add_agent_single!(agent, model)
    return model

and do a parameter scan by doing:

happyperc(moods) = count(moods) / length(moods)
adata = [(:mood, happyperc)]

parameters = Dict(
    :min_to_be_happy => collect(2:5), # expanded
    :numagents => [200, 300],         # expanded
    :griddims => (20, 20),            # not Vector = not expanded

adf, _ = paramscan(parameters, initialize; adata, agent_step!, n = 3)

given position in continuous space, return cell coordinates in grid space.

positions(model::ABM{<:DiscreteSpace}) → ns

Return an iterator over all positions of a model with a discrete space.

positions(model::ABM{<:DiscreteSpace}, by::Symbol) → ns

Return all positions of a model with a discrete space, sorting them using the argument by which can be:

  • :random - randomly sorted
  • :population - positions are sorted depending on how many agents they accommodate. The more populated positions are first.
random_agent(model, condition; optimistic=false) → agent

Return a random agent from the model that satisfies condition(agent) == true. The function generates a random permutation of agent IDs and iterates through them. If no agent satisfies the condition, nothing is returned instead.


optimistic = false changes the algorithm used to be non-allocating but potentially more variable in performance. This should be faster if the condition is true for a large proportion of the population (for example if the agents are split into groups).

random_agent(model) → agent

Return a random agent from the model.

random_empty(model::ABM{<:DiscreteSpace}, cutoff = 0.998)

Return a random position without any agents, or nothing if no such positions exist. cutoff switches the search algorithm from probabilistic to a filter. Specifically, when clamp(nagents(model)/npositions(model), 0.0, 1.0) < cutoff, then the algorithm is probabilistic.

random_nearby_agent(agent, model::ABM, r = 1, f = nothing; kwargs...) → agent

Return a random agent near the position of the given agent or nothing if no agent is nearby.

The value of the argument r and possible keywords operate identically to nearby_ids.

A filter function f(agent) can be passed so that to restrict the sampling on only those agents for which the function returns true.

random_nearby_id(agent, model::ABM, r = 1, f = nothing; kwargs...) → id

Return the id of a random agent near the position of the given agent using an optimized algorithm from Reservoir sampling. Return nothing if no agents are nearby.

The value of the argument r and possible keywords operate identically to nearby_ids.

A filter function f(id) can be passed so that to restrict the sampling on only those ids for which the function returns true.

random_nearby_position(position, model::ABM, r=1, f = nothing; kwargs...) → position

Return a random position near the given position. Return nothing if the space doesn't allow for nearby positions.

The value of the argument r and possible keywords operate identically to nearby_positions.

A filter function f(pos) can be passed so that to restrict the sampling on only those positions for which the function returns true. In this case nothing is also returned if no nearby position satisfies f.

random_position(model) → pos

Return a random position in the model's space (always with appropriate Type).

randomwalk!(agent, model::ABM{<:AbstractGridSpace}, r::Real = 1; kwargs...)

Move agent for a distance r in a random direction respecting boundary conditions and space metric. For Chebyshev and Manhattan metric, the step size r is rounded to floor(Int,r); for Euclidean metric in a GridSpace, random walks are ill defined and hence not supported.

For example, for Chebyshev metric and r=1, this will move the agent with equal probability to any of the 8 surrounding cells. For Manhattan metric, it will move to any of the 4 surrounding cells.


  • ifempty will check that the target position is unoccupied and only move if that's true. So if ifempty is true, this can result in the agent not moving even if there are available positions. By default this is true, set it to false if different agents can occupy the same position. In a GridSpaceSingle, agents cannot overlap anyways and this keyword has no effect.
  • force_motion has an effect only if ifempty is true or the space is a GridSpaceSingle. If set to true, the search for the random walk will be done only on the empty positions, so in this case the agent will always move if there is at least one empty position to choose from. By default this is false.
randomwalk!(agent, model::ABM{<:ContinuousSpace} [, r];
    [polar=Uniform(-π,π), azimuthal=Arccos(-1,1)]

Re-orient and move agent for a distance r in a random direction respecting space boundary conditions. By default r = norm(agent.vel).

The ContinuousSpace version is slightly different than the grid space. Here, the agent's velocity is updated by the random vector generated for the random walk.

Uniform/isotropic random walks are supported in any number of dimensions while an angles distribution can be specified for 2D and 3D random walks. In this case, the velocity vector is rotated using random angles given by the distributions for polar (2D and 3D) and azimuthal (3D only) angles, and scaled to have measure r. After the re-orientation the agent is moved for r in the new direction.

Anything that supports rand can be used as an angle distribution instead. This can be useful to create correlated random walks.

 rem_node!(model::ABM{<: GraphSpace}, n::Int)

Remove node (i.e. position) n from the model's graph. All agents in that node are removed from the model. Warning: Graphs.jl (and thus Agents.jl) swaps the index of the last node with that of the one to be removed, while every other node remains as is. This means that when doing rem_node!(n, model) the last node becomes the n-th node while the previous n-th node (and all its edges and agents) are deleted.

remove_agent!(agent::AbstractAgent, model::ABM)
remove_agent!(id::Int, model::ABM)

Remove an agent from the model.

remove_agent_from_space!(agent, model)

Remove the agent from the underlying space structure. This function is called after the agent is already removed from the model container. This function is NOT part of the public API.

remove_all!(model::ABM, IDs)

Remove the agents with the given IDs.

remove_all!(model::ABM, f::Function)

Remove all agents where the function f(agent) returns true.

remove_all!(model::ABM, n::Int)

Remove the agents whose IDs are larger than n.

replicate!(agent, model; kwargs...)

Add a new agent to the model at the same position of the given agent, copying the values of its fields. With the kwargs it is possible to override the values by specifying new ones for some fields. Return the new agent instance.


using Agents
@agent A GridAgent{2} begin

model = ABM(A, GridSpace((5, 5)))
a = A(1, (2, 2), 0.5, 0.5)
b = replicate!(a, model; w = 0.8)
rotate(w::SVector{2}, θ::Real)

Rotate two-dimensional vector w by an angle θ. The angle must be given in radians.

rotate(w::SVector{3}, θ::Real, ϕ::Real)

Rotate three-dimensional vector w by angles θ (polar) and ϕ (azimuthal). The angles must be given in radians.

Note that in general a 3D rotation requires 1 angle and 1 axis of rotation (or 3 angles). Here, using only 2 angles, w is first rotated by angle θ about an arbitrarily chosen vector (u) normal to it (u⋅w=0); this new rotated vector (a) is then rotated about the original w by the angle ϕ. The resulting vector (v) satisfies (v⋅w)/(|v|*|w|) = cos(θ) ∀ ϕ.

rotate_polygon(p::Polygon, θ)

Rotate given polygon counter-clockwise by θ (in radians).!Function
run!(model, agent_step! [, model_step!], n::Integer; kwargs...) → agent_df, model_df
run!(model, agent_step!, model_step!, n::Function; kwargs...) → agent_df, model_df

Run the model (step it with the input arguments propagated into step!) and collect data specified by the keywords, explained one by one below. Return the data as two DataFrames, one for agent-level data and one for model-level data.

See also offline_run! to write data to file while running the model.

Data-deciding keywords

  • adata::Vector means "agent data to collect". If an entry is a Symbol, e.g. :weight, then the data for this entry is agent's field weight. If an entry is a Function, e.g. f, then the data for this entry is just f(a) for each agent a. The resulting dataframe columns are named with the input symbol (here :weight, :f).

  • adata::Vector{<:Tuple}: if adata is a vector of tuples instead, data aggregation is done over the agent properties.

    For each 2-tuple, the first entry is the "key" (any entry like the ones mentioned above, e.g. :weight, f). The second entry is an aggregating function that aggregates the key, e.g. mean, maximum. So, continuing from the above example, we would have adata = [(:weight, mean), (f, maximum)].

    It's also possible to provide a 3-tuple, with the third entry being a conditional function (returning a Bool), which assesses if each agent should be included in the aggregate. For example: x_pos(a) = a.pos[1]>5 with (:weight, mean, x_pos) will result in the average weight of agents conditional on their x-position being greater than 5.

    The resulting data name columns use the function dataname. They create something like :mean_weight or :maximum_f_x_pos. In addition, you can use anonymous functions in a list comprehension to assign elements of an array into different columns: adata = [(a)->(a.interesting_array[i]) for i=1:N]. Column names can also be renamed with DataFrames.rename! after data is collected.

    Notice: Aggregating only works if there are agents to be aggregated over. If you remove agents during model run, you should modify the aggregating functions. E.g. instead of passing mean, pass mymean(a) = isempty(a) ? 0.0 : mean(a).

  • mdata::Vector means "model data to collect" and works exactly like adata. For the model, no aggregation is possible (nothing to aggregate over).

    Alternatively, mdata can also be a function. This is a "generator" function, that accepts model as input and provides a Vector that represents mdata. Useful in combination with an ensemblerun! call that requires a generator function.

By default both keywords are nothing, i.e. nothing is collected/aggregated.


For mixed-models, the adata keyword has some additional options & properties. An additional column agent_type will be placed in the output dataframe.

In the case that data is needed for one agent type that does not exist in a second agent type, missing values will be added to the dataframe.

Warning: Since this option is inherently type unstable, try to avoid this in a performance critical situation.

Aggregate functions will fail if missing values are not handled explicitly. If a1.weight but a2 (type: Agent2) has no weight, use a2(a) = a isa Agent2; adata = [(:weight, sum, a2)] to filter out the missing results.

Other keywords

  • when=true : at which steps s to perform the data collection and processing. A lot of flexibility is offered based on the type of when. If when::AbstractVector, then data are collected if s ∈ when. Otherwise data are collected if when(model, s) returns true. By default data are collected in every step.
  • when_model = when : same as when but for model data.
  • obtainer = identity : method to transfer collected data to the DataFrame. Typically only change this to copy if some data are mutable containers (e.g. Vector) which change during evolution, or deepcopy if some data are nested mutable containers. Both of these options have performance penalties.
  • agents_first=true : Whether to update agents first and then the model, or vice versa.
  • showprogress=false : Whether to show progress
sample!(model::ABM, n [, weight]; kwargs...)

Replace the agents of the model with a random sample of the current agents with size n.

Optionally, provide a weight: Symbol (agent field) or function (input agent out put number) to weight the sampling. This means that the higher the weight of the agent, the higher the probability that this agent will be chosen in the new sampling.


  • replace = true : whether sampling is performed with replacement, i.e. all agents can

be chosen more than once.

Example usage in Wright-Fisher model of evolution.

scale_polygon(p::Polygon, s)

Scale given polygon by s, assuming polygon's center of reference is the origin.

schedule(model) → ids

Return an iterator over the scheduled IDs using the model's scheduler. Literally equivalent with abmscheduler(model)(model).

seed!(model [, seed])

Reseed the random number pool of the model with the given seed or a random one, when using a pseudo-random number generator like MersenneTwister.


This is called internally by randomwalk! for more performant isotropic/uniform random walks; it also works for any number of dimensions.

walk!(agent, direction::NTuple, model; ifempty = true)

Move agent in the given direction respecting periodic boundary conditions. For non-periodic spaces, agents will walk to, but not exceed the boundary value. Available for both AbstractGridSpace and ContinuousSpaces.

The type of direction must be the same as the space position. AbstractGridSpace asks for Int, and ContinuousSpace for Float64 tuples, describing the walk distance in each direction. direction = (2, -3) is an example of a valid direction on a AbstractGridSpace, which moves the agent to the right 2 positions and down 3 positions. Agent velocity is ignored for this operation in ContinuousSpace.


  • ifempty will check that the target position is unoccupied and only move if that's true. Available only on AbstractGridSpace.

Example usage in Battle Royale.

walk!(agent, rand, model)

Invoke a random walk by providing the rand function in place of direction. For AbstractGridSpace, the walk will cover ±1 positions in all directions, ContinuousSpace will reside within [-1, 1].

This functionality is deprecated. Use randomwalk! instead.

getindex(model::ABM, id::Integer)

Return an agent given its ID.

getproperty(model::ABM, :prop)

Return a property with name :prop from the current model, assuming the model properties are either a dictionary with key type Symbol or a Julia struct. For example, if a model has the set of properties Dict(:weight => 5, :current => false), retrieving these values can be obtained via model.weight.

The property names :agents, :space, :scheduler, :properties, :maxid are internals and should not be accessed by the user. In the next release, getting those will error.

isempty(position, model::ABM{<:DiscreteSpace})

Return true if there are no agents in position.

step!(model::ABM, agent_step!, n::Int = 1)
step!(model::ABM, agent_step!, model_step!, n::Int = 1, agents_first::Bool = true)

Update agents n steps according to the stepping function agent_step!. Agents will be activated as specified by the model.scheduler. model_step! is triggered after every scheduled agent has acted, unless the argument agents_first is false (which then first calls model_step! and then activates the agents).

step! ignores scheduled IDs that do not exist within the model, allowing you to safely remove agents dynamically.

step!(model, agent_step!, model_step!, n::Function, agents_first::Bool = true)

In this version n is a function. Then step! runs the model until n(model, s) returns true, where s is the current amount of steps taken, starting from 0. For this method of step!, model_step! must be provided always (use dummystep if you have no model stepping dynamics).

See also Advanced stepping for stepping complex models where agent_step! might not be convenient.

add_edge!(model::ABM{<:GraphSpace},  args...; kwargs...)

Add a new edge (relationship between two positions) to the graph. Returns a boolean, true if the operation was successful.

args and kwargs are directly passed to the add_edge! dispatch that acts the underlying graph type.


Add a new node (i.e. possible position) to the model's graph and return it. You can connect this new node with existing ones using add_edge!.

rem_edge!(model::ABM{<:GraphSpace}, n, m)

Remove an edge (relationship between two positions) from the graph. Returns a boolean, true if the operation was successful.

rem_vertex!(model::ABM{<:GraphSpace}, n::Int)

Remove node (i.e. position) n from the model's graph. All agents in that node are removed from the model.

Warning: Graphs.jl (and thus Agents.jl) swaps the index of the last node with that of the one to be removed, while every other node remains as is. This means that when doing rem_vertex!(n, model) the last node becomes the n-th node while the previous n-th node (and all its edges and agents) are deleted.


Return the number of edges in the model space.


Return the number of positions (vertices) in the model space.

@agent YourAgentType{X} AnotherAgentType [OptionalSupertype] begin
    # etc...

Define an agent struct which includes all fields that AnotherAgentType has, as well as any additional ones the user may provide via the begin block. See below for examples.

Using @agent is the recommended way to create agent types for Agents.jl, however keep in mind that the macro (currently) doesn't work with Base.@kwdef or const declarations in individual fields (for Julia v1.8+).

Structs created with @agent by default subtype AbstractAgent. They cannot subtype each other, as all structs created from @agent are concrete types and AnotherAgentType itself is also concrete (only concrete types have fields). If you want YourAgentType to subtype something other than AbstractAgent, use the optional argument OptionalSupertype (which itself must then subtype AbstractAgent).


The macro @agent has two primary uses:

  1. To include the mandatory fields for a particular space in your agent struct. In this case you would use one of the minimal agent types as AnotherAgentType.
  2. A convenient way to include fields from another, already existing struct.

The existing minimal agent types are:

All will attribute an id::Int field, and besides NoSpaceAgent will also attribute a pos field. You should never directly manipulate the mandatory fields id, pos that the resulting new agent type will have. The id is an unchangeable field. Use functions like move_agent! etc., to change the position.


Example without optional hierarchy


@agent Person{T} GridAgent{2} begin

will create an agent appropriate for using with 2-dimensional GridSpace

mutable struct Person{T} <: AbstractAgent
    pos::NTuple{2, Int}

and then, one can even do

@agent Baker{T} Person{T} begin

which would make

mutable struct Baker{T} <: AbstractAgent
    pos::NTuple{2, Int}

Example with optional hierarchy

An alternative way to make the above structs, that also establishes a user-specific subtyping hierarchy would be to do:

abstract type AbstractHuman <: AbstractAgent end

@agent Worker GridAgent{2} AbstractHuman begin

@agent Fisher Worker AbstractHuman begin

which would now make both Fisher and Worker subtypes of AbstractHuman.

julia> supertypes(Fisher)
(Fisher, AbstractHuman, AbstractAgent, Any)

julia> supertypes(Worker)
(Worker, AbstractHuman, AbstractAgent, Any)

Note that Fisher will not be a subtype of Worker although Fisher has inherited the fields from Worker.

Example highlighting problems with parametric types

Notice that in Julia parametric types are union types. Hence, the following cannot be used:

@agent Dummy{T} GridAgent{2} begin

@agent Fisherino{T} Dummy{T} begin

You will get an error in the definition of Fisherino, because the fields of Dummy{T} cannot be obtained, because it is a union type. Same with using Dummy. You can only use Dummy{Float64}.

Example with common dispatch and no subtyping

It may be that you do not even need to create a subtyping relation if you want to utilize multiple dispatch. Consider the example:

@agent CommonTraits GridSpace{2} begin

and then two more structs are made from these traits:

@agent Bird CommonTraits begin

@agent Rabbit CommonTraits begin

If you wanted a function that dispatches to both Rabbit, Bird, you only have to define:

Animal = Union{Bird, Rabbit}
f(x::Animal) = ... # uses `CommonTraits` fields

However, it should also be said, that there is no real reason here to explicitly type-annotate x::Animal in f. Don't annotate any type. Annotating a type only becomes useful if there are at least two "abstract" groups, like Animal, Person. Then it would make sense to define

Person = Union{Fisher, Baker}
f(x::Animal) = ... # uses `CommonTraits` fields
f(x::Person) = ... # uses fields that all "persons" have

Submodule containing functionality for serialization and deserialization of model data to and from files.

AgentsIO.dump_to_csv(filename, agents [, fields]; kwargs...)

Dump agents to the CSV file specified by filename. agents is any iterable sequence of types, such as from allagents. fields is an iterable sequence of Symbols specifying which fields of each agent are dumped. If not explicitly specified, it is automatically inferred using eltype(agents). All kwargs... are forwarded to CSV.write.

All Tuple{...} fields are flattened to multiple columns suffixed by _1, _2... similarly to AgentsIO.populate_from_csv!

For example,

struct Foo <: AbstractAgent

model = ABM(Foo, ...)
AgentsIO.dump_to_csv("test.csv", allagents(model))

The resultant "test.csv" file will contain the following columns: id, pos_1, pos_2, foo_1, foo_2.

AgentsIO.from_serializable(t; kwargs...)

Given a value in its serializable form, return the original version. This defaults to the value itself, unless a more specific method is defined. Define a method for this function and for AgentsIO.to_serializable if you need custom serialization for model properties. This also enables passing keyword arguments to AgentsIO.load_checkpoint and having access to them through kwargs.

Refer to AgentsIO.to_serializable for more info.

AgentsIO.load_checkpoint(filename; kwargs...)

Load the model saved to the file specified by filename.


  • scheduler = Schedulers.fastest specifies what scheduler should be used for the model.
  • warn = true can be used to disable warnings from type checks on the agent type.

ContinuousSpace specific:

  • update_vel! specifies a function that should be used to update each agent's velocity before it is moved. Refer to ContinuousSpace for details.

OpenStreetMapSpace specific:

  • map is a path to the OpenStreetMap to be used for the space. This is a required parameter if the space is OpenStreetMapSpace.
  • use_cache = false, trim_to_connected_graph = true refer to OpenStreetMapSpace
AgentsIO.populate_from_csv!(model, filename [, agent_type, col_map]; row_number_is_id, kwargs...)

Populate the given model using CSV data contained in filename. Use agent_type to specify the type of agent to create (In the case of multi-agent models) or a function that returns an agent to add to the model. The CSV row is splatted into the agent_type constructor/function.

col_map is a Dict{Symbol,Int} specifying a mapping of keyword-arguments to row number. If col_map is specified, the specified data is splatted as keyword arguments.

The keyword row_number_is_id = false specifies whether the row number will be passed as the first argument (or as id keyword) to agent_type.

Any other keyword arguments are forwarded to CSV.Rows. If the types keyword is not specified and agent_type is a struct, then the mapping from struct field to type will be used. Tuple{...} fields will be suffixed with _1, _2, ... similarly to AgentsIO.dump_to_csv

For example,

struct Foo <: AbstractAgent

model = ABM(Foo, ...)
AgentsIO.populate_from_csv!(model, "test.csv")

Here, types will be inferred to be

    :id => Int,
    :pos_1 => Int,
    :pos_2 => Int,
    :foo_1 => Int,
    :foo_2 => String,

It is not necessary for all these fields to be present as columns in the CSV. Any column names that match will be converted to the appropriate type. There should exist a constructor for Foo taking the appropriate combination of fields as parameters.

If "test.csv" contains the following columns: pos_1, pos_2, foo_1, foo_2, then model can be populated as AgentsIO.populate_from_csv!(model, "test.csv"; row_number_is_id = true).

AgentsIO.save_checkpoint(filename, model::ABM)

Write the entire model to file specified by filename. The following points should be considered before using this functionality:

  • OpenStreetMap data is not saved. The path to the map should be specified when loading the model using the map keyword of AgentsIO.load_checkpoint.
  • Functions are not saved, including stepping functions, schedulers, and update_vel!. The last two can be provided to AgentsIO.load_checkpoint using the appropriate keyword arguments.

Return the serializable form of the passed value. This defaults to the value itself, unless a more specific method is defined. Define a method for this function and for AgentsIO.from_serializable if you need custom serialization for model properties. This also enables passing keyword arguments to AgentsIO.load_checkpoint and having access to them during deserialization of the properties. Some possible scenarios where this may be required are:

  • Your properties contain functions (or any type not supported by JLD2.jl). These may not be (de)serialized correctly. This could result in checkpoint files that cannot be loaded back in, or contain reconstructed types that do not retain their data/functionality.
  • Your properties contain data that can be recalculated during deserialization. Omitting such properties can reduce the size of the checkpoint file, at the expense of some extra computation at deserialization.

If your model properties do not fall in the above scenarios, you do not need to use this function.

This function, and AgentsIO.from_serializable is not called recursively on every type/value during serialization. The final serialization functionality is enabled by JLD2.jl. To define custom serialization for every occurrence of a specific type (such as agent structs), refer to the Custom Serialization section of JLD2.jl documentation.


Submodule containing functionality for path-finding based on the A* algorithm. Currently available for GridSpace and ContinuousSpace. Discretization of ContinuousSpace is taken care of internally.

You can enable path-finding and set its options by creating an instance of a Pathfinding.AStar struct. This must be passed to the relevant pathfinding functions during the simulation. Call plan_route! to set the destination for an agent. This triggers the algorithm to calculate a path from the agent's current position to the one specified. You can alternatively use plan_best_route! to choose the best target from a list. Once a target has been set, you can move an agent one step along its precalculated path using the move_along_route! function.

Refer to the Maze Solver, Mountain Runners and Rabbit, Fox, Hawk examples using path-finding and see the available functions below as well.

Pathfinding.AStar(space; kwargs...)

Enables pathfinding for agents in the provided space (which can be a GridSpace or ContinuousSpace) using the A* algorithm. This struct must be passed into any pathfinding functions.

For ContinuousSpace, a walkmap or instance of PenaltyMap must be provided to specify the level of discretisation of the space.


  • diagonal_movement = true specifies if movement can be to diagonal neighbors of a tile, or only orthogonal neighbors. Only available for GridSpace
  • admissibility = 0.0 allows the algorithm to approximate paths to speed up pathfinding. A value of admissibility allows paths with at most (1+admissibility) times the optimal length.
  • walkmap = trues(size(space)) specifies the (un)walkable positions of the space. If specified, it should be a BitArray of the same size as the corresponding GridSpace. By default, agents can walk anywhere in the space.
  • cost_metric = DirectDistance{D}() is an instance of a cost metric and specifies the metric used to approximate the distance between any two points.

Utilization of all features of AStar occurs in the 3D Mixed-Agent Ecosystem with Pathfinding example.


An abstract type representing a metric that measures the approximate cost of travelling between two points in a D dimensional grid.

Pathfinding.DirectDistance{D}([direction_costs::Vector{Int}]) <: CostMetric{D}

Distance is approximated as the shortest path between the two points, provided the walkable property of Pathfinding.AStar allows. Optionally provide a Vector{Int} that represents the cost of going from a tile to the neighboring tile on the i dimensional diagonal (default is 10√i).

If diagonal_movement=false in Pathfinding.AStar, neighbors in diagonal positions will be excluded. Cost defaults to the first value of the provided vector.

Pathfinding.MaxDistance{D}() <: CostMetric{D}

Distance between two tiles is approximated as the maximum of absolute difference in coordinates between them.


Alias of MutableLinkedList{NTuple{D,T}}. Used to represent the path to be taken by an agent in a D dimensional space.

Pathfinding.PenaltyMap(pmap::Array{Int,D} [, base_metric::CostMetric]) <: CostMetric{D}

Distance between two positions is the sum of the shortest distance between them and the absolute difference in penalty.

A penalty map (pmap) is required. For pathfinding in GridSpace, this should be the same dimensions as the space. For pathfinding in ContinuousSpace, the size of this map determines the granularity of the underlying grid, and should agree with the size of the walkable map.

Distance is calculated using Pathfinding.DirectDistance by default, and can be changed by specifying base_metric.

An example usage can be found in Mountain Runners.

Pathfinding.delta_cost(pathfinder::GridPathfinder{D}, metric::M, from, to) where {M<:CostMetric}

Calculate an approximation for the cost of travelling from from to to (both of type NTuple{N,Int}. Expects a return value of Float64.

find_continuous_path(pathfinder, from, to)

Functions like find_path, but uses the output of find_path and converts it to the coordinate space used by the corresponding ContinuousSpace. Performs checks on the last two waypoints in the discrete path to ensure continuous path is optimal.

find_path(pathfinder::AStar{D}, from::NTuple{D,Int}, to::NTuple{D,Int})

Calculate the shortest path from from to to using the A* algorithm. If a path does not exist between the given positions, an empty linked list is returned.

Pathfinding.nearby_walkable(position, model::ABM{<:GridSpace{D}}, pathfinder::AStar{D}, r = 1)

Return an iterator over all nearby_positions within "radius" r of the given position (excluding position), which are walkable as specified by the given pathfinder.


Return the penalty map of a Pathfinding.AStar if the Pathfinding.PenaltyMap metric is in use, nothing otherwise.

It is possible to mutate the map directly, for example Pathfinding.penaltymap(pathfinder)[15, 40] = 115 or Pathfinding.penaltymap(pathfinder) .= rand(50, 50). If this is mutated, a new path needs to be planned using plan_route!.

Pathfinding.position_delta(pathfinder::AStar{D}, from::NTuple{Int,D}, to::NTuple{Int,D})

Returns the absolute difference in coordinates between from and to taking into account periodicity of pathfinder.

Pathfinding.random_walkable(model, pathfinder::AStar{D})

Return a random position in the given model that is walkable as specified by the given pathfinder.

Pathfinding.random_walkable(pos, model::ABM{<:ContinuousSpace{D}}, pathfinder::AStar{D}, r = 1.0)

Return a random position within radius r of pos which is walkable, as specified by pathfinder. Return pos if no such position exists.

move_along_route!(agent, model::ABM{<:ContinuousSpace{D}}, pathfinder::AStar{D}, speed, dt = 1.0)

Move agent for one step along the route toward its target set by plan_route! at the given speed and timestep dt.

For pathfinding in models with ContinuousSpace

If the agent does not have a precalculated path or the path is empty, it remains stationary.

move_along_route!(agent, model::ABM{<:GridSpace{D}}, pathfinder::AStar{D})

Move agent for one step along the route toward its target set by plan_route!

For pathfinding in models with GridSpace.

If the agent does not have a precalculated path or the path is empty, it remains stationary.

plan_best_route!(agent, dests, pathfinder::AStar{D}; kwargs...)

Calculate, store, and return the best path to move the agent from its current position to a chosen destination taken from dests using pathfinder.

The condition = :shortest keyword retuns the shortest path which is shortest out of the possible destinations. Alternatively, the :longest path may also be requested.

Return the position of the chosen destination. Return nothing if none of the supplied destinations are reachable.

plan_route!(agent, dest, pathfinder::AStar{D})

Calculate and store the shortest path to move the agent from its current position to dest (a position e.g. (1, 5) or (1.3, 5.2)) using the provided pathfinder.

Use this method in conjuction with move_along_route!.

Pathfinding.remove_agent!(agent, model, pathfinder)

The same as remove_agent!(agent, model), but also removes the agent's path data from pathfinder.


Sub-module of the module Agents, which contains pre-defined agent based models shown the the Examples section of the documentation.

Models are represented by functions that initialize an ABM, and return the ABM, and the agent and model stepping functions.

    n_birds = 100,
    speed = 1.0,
    cohere_factor = 0.25,
    separation = 4.0,
    separate_factor = 0.25,
    match_factor = 0.01,
    visual_distance = 5.0,
    extent = (100, 100),
    spacing = visual_distance / 1.5

Same as in Flocking model.

    C = 8,
    max_travel_rate = 0.01,
    Ns = rand(50:5000, C),
    β_und = rand(0.3:0.02:0.6, C),
    β_det = β_und ./ 10,
    infection_period = 30,
    reinfection_probability = 0.05,
    detection_time = 14,
    death_rate = 0.02,
    Is = [zeros(Int, length(Ns) - 1)..., 1],
    seed = 19,

Same as in SIR model for the spread of COVID-19.