track unique_id automatically

benchmarks/BoltzmannWealth/boltzmann_wealth.py

@@ -27,8 +27,8 @@ def __init__(self, seed=None, n=100, width=10, height=10):
             model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
         )
         # Create agents
-        for i in range(self.num_agents):
-            a = MoneyAgent(i, self)
+        for _ in range(self.num_agents):
+            a = MoneyAgent(self)
             # Add the agent to a random grid cell
             x = self.random.randrange(self.grid.width)
             y = self.random.randrange(self.grid.height)
@@ -50,8 +50,8 @@ def run_model(self, n):
 class MoneyAgent(mesa.Agent):
     """An agent with fixed initial wealth."""
 
-    def __init__(self, unique_id, model):
-        super().__init__(unique_id, model)
+    def __init__(self, model):
+        super().__init__(model)
         self.wealth = 1
 
     def move(self):

benchmarks/Flocking/flocking.py

@@ -27,7 +27,6 @@ class Boid(mesa.Agent):
 
     def __init__(
         self,
-        unique_id,
         model,
         speed,
         direction,
@@ -41,7 +40,6 @@ def __init__(
         Create a new Boid flocker agent.
 
         Args:
-            unique_id: Unique agent identifier.
             speed: Distance to move per step.
             direction: numpy vector for the Boid's direction of movement.
             vision: Radius to look around for nearby Boids.
@@ -51,7 +49,7 @@ def __init__(
             match: the relative importance of matching neighbors' directions
 
         """
-        super().__init__(unique_id, model)
+        super().__init__(model)
         self.speed = speed
         self.direction = direction
         self.vision = vision
@@ -131,13 +129,12 @@ def __init__(
             "match": match,
         }
 
-        for i in range(self.population):
+        for _ in range(self.population):
             x = self.random.random() * self.space.x_max
             y = self.random.random() * self.space.y_max
             pos = np.array((x, y))
             direction = np.random.random(2) * 2 - 1
             boid = Boid(
-                unique_id=i,
                 model=self,
                 speed=speed,
                 direction=direction,

benchmarks/Schelling/schelling.py

@@ -8,15 +8,14 @@ class SchellingAgent(CellAgent):
     Schelling segregation agent
     """
 
-    def __init__(self, unique_id, model, agent_type, radius, homophily):
+    def __init__(self, model, agent_type, radius, homophily):
         """
         Create a new Schelling agent.
         Args:
-           unique_id: Unique identifier for the agent.
            x, y: Agent initial location.
            agent_type: Indicator for the agent's type (minority=1, majority=0)
         """
-        super().__init__(unique_id, model)
+        super().__init__(model)
         self.type = agent_type
         self.radius = radius
         self.homophily = homophily
@@ -81,9 +80,7 @@ def __init__(
         for cell in self.grid:
             if self.random.random() < density:
                 agent_type = 1 if self.random.random() < self.minority_pc else 0
-                agent = SchellingAgent(
-                    self.next_id(), self, agent_type, radius, homophily
-                )
+                agent = SchellingAgent(self, agent_type, radius, homophily)
                 agent.move_to(cell)
                 self.schedule.add(agent)
 

benchmarks/WolfSheep/wolf_sheep.py

@@ -17,8 +17,8 @@
 
 
 class Animal(CellAgent):
-    def __init__(self, unique_id, model, energy, p_reproduce, energy_from_food):
-        super().__init__(unique_id, model)
+    def __init__(self, model, energy, p_reproduce, energy_from_food):
+        super().__init__(model)
         self.energy = energy
         self.p_reproduce = p_reproduce
         self.energy_from_food = energy_from_food
@@ -29,7 +29,6 @@ def random_move(self):
     def spawn_offspring(self):
         self.energy /= 2
         offspring = self.__class__(
-            self.model.next_id(),
             self.model,
             self.energy,
             self.p_reproduce,
@@ -107,7 +106,7 @@ def fully_grown(self, value: bool) -> None:
                 function_args=[self, "fully_grown", True],
             )
 
-    def __init__(self, unique_id, model, fully_grown, countdown, grass_regrowth_time):
+    def __init__(self, model, fully_grown, countdown, grass_regrowth_time):
         """
         TODO:: fully grown can just be an int --> so one less param (i.e. countdown)
 
@@ -119,7 +118,7 @@ def __init__(self, unique_id, model, fully_grown, countdown, grass_regrowth_time
             grass_regrowth_time : time to fully regrow grass
             countdown : Time for the patch of grass to be fully regrown if fully grown is False
         """
-        super().__init__(unique_id, model)
+        super().__init__(model)
         self._fully_grown = fully_grown
         self.grass_regrowth_time = grass_regrowth_time
 
@@ -189,7 +188,6 @@ def __init__(
             )
             energy = self.random.randrange(2 * sheep_gain_from_food)
             sheep = Sheep(
-                self.next_id(),
                 self,
                 energy,
                 sheep_reproduce,
@@ -205,7 +203,6 @@ def __init__(
             )
             energy = self.random.randrange(2 * wolf_gain_from_food)
             wolf = Wolf(
-                self.next_id(),
                 self,
                 energy,
                 wolf_reproduce,
@@ -221,9 +218,7 @@ def __init__(
                 countdown = grass_regrowth_time
             else:
                 countdown = self.random.randrange(grass_regrowth_time)
-            patch = GrassPatch(
-                self.next_id(), self, fully_grown, countdown, grass_regrowth_time
-            )
+            patch = GrassPatch(self, fully_grown, countdown, grass_regrowth_time)
             patch.move_to(cell)
 
     def step(self):

docs/tutorials/MoneyModel.py

@@ -15,8 +15,8 @@ def compute_gini(model):
 class MoneyAgent(mesa.Agent):
     """An agent with fixed initial wealth."""
 
-    def __init__(self, unique_id, model):
-        super().__init__(unique_id, model)
+    def __init__(self, model):
+        super().__init__(model)
         self.wealth = 1
 
     def move(self):

mesa/agent.py

@@ -10,6 +10,8 @@
 
 import contextlib
 import copy
+import functools
+import itertools
 import operator
 import warnings
 import weakref
@@ -32,21 +34,49 @@ class Agent:
     Base class for a model agent in Mesa.
 
     Attributes:
-        unique_id (int): A unique identifier for this agent.
         model (Model): A reference to the model instance.
-        self.pos: Position | None = None
+        unique_id (int): A unique identifier for this agent.
+        pos (Position): A reference to the position where this agent is located.
+
+    Notes:
+          unique_id is unique relative to a model instance and starts from 1
+
     """
 
-    def __init__(self, unique_id: int, model: Model) -> None:
+    # this is a class level attribute
+    # it is a dictionary, indexed by model instance
+    # so, unique_id is unique relative to a model, and counting starts from 1
+    _ids = defaultdict(functools.partial(itertools.count, 1))
+
+    def __init__(self, *args, **kwargs) -> None:
         """
         Create a new agent.
 
         Args:
-            unique_id (int): A unique identifier for this agent.
             model (Model): The model instance in which the agent exists.
         """
-        self.unique_id = unique_id
-        self.model = model
+        # TODO: Cleanup in future Mesa version (3.1+)
+        match args:
+            # Case 1: Only the model is provided. The new correct behavior.
+            case [model]:
+                self.model = model
+                self.unique_id = next(self._ids[model])
+            # Case 2: Both unique_id and model are provided, deprecated
+            case [_, model]:
+                warnings.warn(
+                    "unique ids are assigned automatically to Agents in Mesa 3. The use of custom unique_id is "
+                    "deprecated. Only input a model when calling `super()__init__(model)`. The unique_id inputted is not used.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+                self.model = model
+                self.unique_id = next(self._ids[model])
+            # Case 3: Anything else, raise an error
+            case _:
+                raise ValueError(
+                    "Invalid arguments provided to initialize the Agent. Only input a model: `super()__init__(model)`."
+                )
+
         self.pos: Position | None = None
 
         self.model.register_agent(self)

mesa/experimental/cell_space/cell_agent.py

@@ -19,15 +19,15 @@ class CellAgent(Agent):
         cell: (Cell | None): the cell which the agent occupies
     """
 
-    def __init__(self, unique_id: int, model: Model) -> None:
+    def __init__(self, model: Model) -> None:
         """
         Create a new agent.
 
         Args:
             unique_id (int): A unique identifier for this agent.
             model (Model): The model instance in which the agent exists.
         """
-        super().__init__(unique_id, model)
+        super().__init__(model)
         self.cell: Cell | None = None
 
     def move_to(self, cell) -> None:

mesa/experimental/devs/examples/epstein_civil_violence.py

@@ -7,8 +7,8 @@
 
 
 class EpsteinAgent(Agent):
-    def __init__(self, unique_id, model, vision, movement):
-        super().__init__(unique_id, model)
+    def __init__(self, model, vision, movement):
+        super().__init__(model)
         self.vision = vision
         self.movement = movement
 
@@ -46,7 +46,6 @@ class Citizen(EpsteinAgent):
 
     def __init__(
         self,
-        unique_id,
         model,
         vision,
         movement,
@@ -59,7 +58,6 @@ def __init__(
         """
         Create a new Citizen.
         Args:
-            unique_id: unique int
             model : model instance
             hardship: Agent's 'perceived hardship (i.e., physical or economic
                 privation).' Exogenous, drawn from U(0,1).
@@ -71,7 +69,7 @@ def __init__(
             vision: number of cells in each direction (N, S, E and W) that
                 agent can inspect. Exogenous.
         """
-        super().__init__(unique_id, model, vision, movement)
+        super().__init__(model, vision, movement)
         self.hardship = hardship
         self.regime_legitimacy = regime_legitimacy
         self.risk_aversion = risk_aversion
@@ -144,8 +142,8 @@ class Cop(EpsteinAgent):
             able to inspect
     """
 
-    def __init__(self, unique_id, model, vision, movement, max_jail_term):
-        super().__init__(unique_id, model, vision, movement)
+    def __init__(self, model, vision, movement, max_jail_term):
+        super().__init__(model, vision, movement)
         self.max_jail_term = max_jail_term
 
     def step(self):
@@ -236,15 +234,13 @@ def __init__(
         for _, pos in self.grid.coord_iter():
             if self.random.random() < self.cop_density:
                 agent = Cop(
-                    self.next_id(),
                     self,
                     cop_vision,
                     movement,
                     max_jail_term,
                 )
             elif self.random.random() < (self.cop_density + self.citizen_density):
                 agent = Citizen(
-                    self.next_id(),
                     self,
                     citizen_vision,
                     movement,
@@ -270,4 +266,4 @@ def step(self):
 
     simulator.setup(model)
 
-    simulator.run(time_delta=100)
+    simulator.run_for(time_delta=100)

mesa/experimental/devs/examples/wolf_sheep.py

@@ -14,8 +14,8 @@
 
 
 class Animal(mesa.Agent):
-    def __init__(self, unique_id, model, moore, energy, p_reproduce, energy_from_food):
-        super().__init__(unique_id, model)
+    def __init__(self, model, moore, energy, p_reproduce, energy_from_food):
+        super().__init__(model)
         self.energy = energy
         self.p_reproduce = p_reproduce
         self.energy_from_food = energy_from_food
@@ -30,7 +30,6 @@ def random_move(self):
     def spawn_offspring(self):
         self.energy /= 2
         offspring = self.__class__(
-            self.model.next_id(),
             self.model,
             self.moore,
             self.energy,
@@ -109,15 +108,15 @@ def fully_grown(self, value: bool):
                 function_args=[self, "fully_grown", True],
             )
 
-    def __init__(self, unique_id, model, fully_grown, countdown, grass_regrowth_time):
+    def __init__(self, model, fully_grown, countdown, grass_regrowth_time):
         """
         Creates a new patch of grass
 
         Args:
             grown: (boolean) Whether the patch of grass is fully grown or not
             countdown: Time for the patch of grass to be fully grown again
         """
-        super().__init__(unique_id, model)
+        super().__init__(model)
         self._fully_grown = fully_grown
         self.grass_regrowth_time = grass_regrowth_time
 
@@ -191,7 +190,6 @@ def __init__(
             )
             energy = self.random.randrange(2 * sheep_gain_from_food)
             sheep = Sheep(
-                self.next_id(),
                 self,
                 moore,
                 energy,
@@ -208,7 +206,6 @@ def __init__(
             )
             energy = self.random.randrange(2 * wolf_gain_from_food)
             wolf = Wolf(
-                self.next_id(),
                 self,
                 moore,
                 energy,
@@ -225,9 +222,7 @@ def __init__(
                 countdown = grass_regrowth_time
             else:
                 countdown = self.random.randrange(grass_regrowth_time)
-            patch = GrassPatch(
-                self.next_id(), self, fully_grown, countdown, grass_regrowth_time
-            )
+            patch = GrassPatch(self, fully_grown, countdown, grass_regrowth_time)
             self.grid.place_agent(patch, pos)
 
     def step(self):