Replace the remaining schedulers with AgentSet functionality (#202)

This PR completes the migration from schedulers to AgentSet functionality across the mesa-examples repository for all regular (non-`gis`/-`rl`) examples. Key changes include:

- Replaced `RandomActivation`, `SimultaneousActivation`, and `RandomActivationByType` schedulers with appropriate AgentSet methods
- Updated `Model.step()` implementations to use AgentSet activation
- Removed references to `schedule.steps`, `schedule.agents`, and `schedule.agents_by_type`
- Updated agent addition/removal logic to work with AgentSets
- Adjusted data collection and visualization code to use `Model.steps` and `Model.agents`

For more details on migrating from schedulers to AgentSets, see the migration guide: https://mesa.readthedocs.io/en/latest/migration_guide.html#time-and-schedulers

examples/pd_grid/analysis.ipynb

@@ -72,7 +72,7 @@
     "            grid[y][x] = 0\n",
     "    ax.pcolormesh(grid, cmap=bwr, vmin=0, vmax=1)\n",
     "    ax.axis(\"off\")\n",
-    "    ax.set_title(f\"Steps: {model.schedule.steps}\")"
+    "    ax.set_title(f\"Steps: {model.steps}\")"
    ]
   },
   {

examples/pd_grid/pd_grid/agent.py

@@ -33,7 +33,7 @@ def step(self):
         best_neighbor = max(neighbors, key=lambda a: a.score)
         self.next_move = best_neighbor.move
 
-        if self.model.schedule_type != "Simultaneous":
+        if self.model.activation_order != "Simultaneous":
             self.advance()
 
     def advance(self):
@@ -42,7 +42,7 @@ def advance(self):
 
     def increment_score(self):
         neighbors = self.model.grid.get_neighbors(self.pos, True)
-        if self.model.schedule_type == "Simultaneous":
+        if self.model.activation_order == "Simultaneous":
             moves = [neighbor.next_move for neighbor in neighbors]
         else:
             moves = [neighbor.move for neighbor in neighbors]

examples/pd_grid/pd_grid/model.py

@@ -6,45 +6,39 @@
 class PdGrid(mesa.Model):
     """Model class for iterated, spatial prisoner's dilemma model."""
 
-    schedule_types = {
-        "Sequential": mesa.time.BaseScheduler,
-        "Random": mesa.time.RandomActivation,
-        "Simultaneous": mesa.time.SimultaneousActivation,
-    }
+    activation_regimes = ["Sequential", "Random", "Simultaneous"]
 
     # This dictionary holds the payoff for this agent,
     # keyed on: (my_move, other_move)
 
     payoff = {("C", "C"): 1, ("C", "D"): 0, ("D", "C"): 1.6, ("D", "D"): 0}
 
     def __init__(
-        self, width=50, height=50, schedule_type="Random", payoffs=None, seed=None
+        self, width=50, height=50, activation_order="Random", payoffs=None, seed=None
     ):
         """
         Create a new Spatial Prisoners' Dilemma Model.
 
         Args:
             width, height: Grid size. There will be one agent per grid cell.
-            schedule_type: Can be "Sequential", "Random", or "Simultaneous".
+            activation_order: Can be "Sequential", "Random", or "Simultaneous".
                            Determines the agent activation regime.
             payoffs: (optional) Dictionary of (move, neighbor_move) payoffs.
         """
         super().__init__()
+        self.activation_order = activation_order
         self.grid = mesa.space.SingleGrid(width, height, torus=True)
-        self.schedule_type = schedule_type
-        self.schedule = self.schedule_types[self.schedule_type](self)
 
         # Create agents
         for x in range(width):
             for y in range(height):
                 agent = PDAgent(self)
                 self.grid.place_agent(agent, (x, y))
-                self.schedule.add(agent)
 
         self.datacollector = mesa.DataCollector(
             {
                 "Cooperating_Agents": lambda m: len(
-                    [a for a in m.schedule.agents if a.move == "C"]
+                    [a for a in m.agents if a.move == "C"]
                 )
             }
         )
@@ -53,8 +47,19 @@ def __init__(
         self.datacollector.collect(self)
 
     def step(self):
-        self.schedule.step()
-        # collect data
+        # Activate all agents, based on the activation regime
+        match self.activation_order:
+            case "Sequential":
+                self.agents.do("step")
+            case "Random":
+                self.agents.shuffle_do("step")
+            case "Simultaneous":
+                self.agents.do("step")
+                self.agents.do("advance")
+            case _:
+                raise ValueError(f"Unknown activation order: {self.activation_order}")
+
+        # Collect data
         self.datacollector.collect(self)
 
     def run(self, n):

examples/pd_grid/pd_grid/server.py

@@ -9,10 +9,10 @@
 model_params = {
     "height": 50,
     "width": 50,
-    "schedule_type": mesa.visualization.Choice(
-        "Scheduler type",
+    "activation_order": mesa.visualization.Choice(
+        "Activation regime",
         value="Random",
-        choices=list(PdGrid.schedule_types.keys()),
+        choices=PdGrid.activation_regimes,
     ),
 }
 

examples/pd_grid/readme.md

@@ -28,7 +28,7 @@ Launch the ``Demographic Prisoner's Dilemma Activation Schedule.ipynb`` notebook
 ## Files
 
 * ``run.py`` is the entry point for the font-end simulations.
-* ``pd_grid/``: contains the model and agent classes; the model takes a ``schedule_type`` string as an argument, which determines what schedule type the model uses: Sequential, Random or Simultaneous.
+* ``pd_grid/``: contains the model and agent classes; the model takes a ``activation_order`` string as an argument, which determines in which order agents are activated: Sequential, Random or Simultaneous.
 * ``Demographic Prisoner's Dilemma Activation Schedule.ipynb``: Jupyter Notebook for running the scheduling experiment. This runs the model three times, one for each activation type, and demonstrates how the activation regime drives the model to different outcomes.
 
 ## Further Reading

examples/schelling/analysis.ipynb

@@ -65,9 +65,9 @@
     }
    ],
    "source": [
-    "while model.running and model.schedule.steps < 100:\n",
+    "while model.running and model.steps < 100:\n",
     "    model.step()\n",
-    "print(model.schedule.steps)  # Show how many steps have actually run"
+    "print(model.steps)  # Show how many steps have actually run"
    ]
   },
   {
@@ -328,15 +328,15 @@
     "    Find the % of agents that only have neighbors of their same type.\n",
     "    \"\"\"\n",
     "    segregated_agents = 0\n",
-    "    for agent in model.schedule.agents:\n",
+    "    for agent in model.agents:\n",
     "        segregated = True\n",
     "        for neighbor in model.grid.iter_neighbors(agent.pos, True):\n",
     "            if neighbor.type != agent.type:\n",
     "                segregated = False\n",
     "                break\n",
     "        if segregated:\n",
     "            segregated_agents += 1\n",
-    "    return segregated_agents / model.schedule.get_agent_count()"
+    "    return segregated_agents / len(model.agents)"
    ]
   },
   {

examples/schelling_experimental/analysis.ipynb

@@ -65,9 +65,9 @@
     }
    ],
    "source": [
-    "while model.running and model.schedule.steps < 100:\n",
+    "while model.running and model.steps < 100:\n",
     "    model.step()\n",
-    "print(model.schedule.steps)  # Show how many steps have actually run"
+    "print(model.steps)  # Show how many steps have actually run"
    ]
   },
   {
@@ -328,15 +328,15 @@
     "    Find the % of agents that only have neighbors of their same type.\n",
     "    \"\"\"\n",
     "    segregated_agents = 0\n",
-    "    for agent in model.schedule.agents:\n",
+    "    for agent in model.agents:\n",
     "        segregated = True\n",
     "        for neighbor in model.grid.iter_neighbors(agent.pos, True):\n",
     "            if neighbor.type != agent.type:\n",
     "                segregated = False\n",
     "                break\n",
     "        if segregated:\n",
     "            segregated_agents += 1\n",
-    "    return segregated_agents / model.schedule.get_agent_count()"
+    "    return segregated_agents / len(model.agents)"
    ]
   },
   {

examples/sugarscape_cg/Readme.md

@@ -21,7 +21,7 @@ The model is tests and demonstrates several Mesa concepts and features:
  - MultiGrid
  - Multiple agent types (ants, sugar patches)
  - Overlay arbitrary text (wolf's energy) on agent's shapes while drawing on CanvasGrid
- - Dynamically removing agents from the grid and schedule when they die
+ - Dynamically removing agents from the grid and model when they die
 
 ## Installation
 
@@ -44,7 +44,6 @@ Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and p
 ## Files
 
 * ``sugarscape/agents.py``: Defines the SsAgent, and Sugar agent classes.
-* ``sugarscape/schedule.py``: This is exactly based on wolf_sheep/schedule.py.
 * ``sugarscape/model.py``: Defines the Sugarscape Constant Growback model itself
 * ``sugarscape/server.py``: Sets up the interactive visualization server
 * ``run.py``: Launches a model visualization server.

examples/sugarscape_cg/sugarscape_cg/agents.py

@@ -67,7 +67,7 @@ def step(self):
         self.eat()
         if self.sugar <= 0:
             self.model.grid.remove_agent(self)
-            self.model.schedule.remove(self)
+            self.remove()
 
 
 class Sugar(mesa.Agent):

examples/sugarscape_cg/sugarscape_cg/model.py

@@ -37,10 +37,9 @@ def __init__(self, width=50, height=50, initial_population=100):
         self.height = height
         self.initial_population = initial_population
 
-        self.schedule = mesa.time.RandomActivationByType(self)
         self.grid = mesa.space.MultiGrid(self.width, self.height, torus=False)
         self.datacollector = mesa.DataCollector(
-            {"SsAgent": lambda m: m.schedule.get_type_count(SsAgent)}
+            {"SsAgent": lambda m: len(m.agents_by_type[SsAgent])}
         )
 
         # Create sugar
@@ -51,7 +50,6 @@ def __init__(self, width=50, height=50, initial_population=100):
             max_sugar = sugar_distribution[x, y]
             sugar = Sugar(self, max_sugar)
             self.grid.place_agent(sugar, (x, y))
-            self.schedule.add(sugar)
 
         # Create agent:
         for i in range(self.initial_population):
@@ -62,31 +60,29 @@ def __init__(self, width=50, height=50, initial_population=100):
             vision = self.random.randrange(1, 6)
             ssa = SsAgent(self, False, sugar, metabolism, vision)
             self.grid.place_agent(ssa, (x, y))
-            self.schedule.add(ssa)
 
         self.running = True
         self.datacollector.collect(self)
 
     def step(self):
-        self.schedule.step()
+        # Step suger and agents
+        self.agents_by_type[Sugar].do("step")
+        self.agents_by_type[SsAgent].shuffle_do("step")
         # collect data
         self.datacollector.collect(self)
         if self.verbose:
-            print([self.schedule.time, self.schedule.get_type_count(SsAgent)])
+            print(f"Step: {self.steps}, SsAgents: {len(self.agents_by_type[SsAgent])}")
 
     def run_model(self, step_count=200):
         if self.verbose:
             print(
-                "Initial number Sugarscape Agent: ",
-                self.schedule.get_type_count(SsAgent),
+                f"Initial number Sugarscape Agents: {len(self.agents_by_type[SsAgent])}"
             )
 
         for i in range(step_count):
             self.step()
 
         if self.verbose:
-            print("")
             print(
-                "Final number Sugarscape Agent: ",
-                self.schedule.get_type_count(SsAgent),
+                f"\nFinal number Sugarscape Agents: {len(self.agents_by_type[SsAgent])}"
             )

examples/sugarscape_g1mt/tests.py

@@ -23,7 +23,7 @@ def test_decreasing_price_variance():
     model.datacollector._new_model_reporter(
         "price_variance",
         lambda m: np.var(
-            flatten([a.prices for a in m.schedule.agents_by_type[Trader].values()])
+            flatten([a.prices for a in m.agents_by_type[Trader].values()])
         ),
     )
     model.run_model(step_count=50)
@@ -40,7 +40,7 @@ def calculate_carrying_capacities(enable_trade):
         for vision_max in visions:
             model = SugarscapeG1mt(vision_max=vision_max, enable_trade=enable_trade)
             model.run_model(step_count=50)
-            carrying_capacities.append(len(model.schedule.agents_by_type[Trader]))
+            carrying_capacities.append(len(model.agents_by_type[Trader]))
         return carrying_capacities
 
     # Carrying capacity should increase over mean vision (figure IV-6).

examples/wolf_sheep/wolf_sheep/test_random_walk.py

@@ -5,7 +5,6 @@
 
 from mesa import Model
 from mesa.space import MultiGrid
-from mesa.time import RandomActivation
 from mesa.visualization.TextVisualization import TextGrid, TextVisualization
 from wolf_sheep.random_walk import RandomWalker
 
@@ -40,17 +39,15 @@ def __init__(self, width, height, agent_count):
         self.grid = MultiGrid(self.width, self.height, torus=True)
         self.agent_count = agent_count
 
-        self.schedule = RandomActivation(self)
         # Create agents
         for i in range(self.agent_count):
             x = self.random.randrange(self.width)
             y = self.random.randrange(self.height)
             a = WalkerAgent(i, (x, y), self, True)
-            self.schedule.add(a)
             self.grid.place_agent(a, (x, y))
 
     def step(self):
-        self.schedule.step()
+        self.agents.shuffle_do("step")
 
 
 class WalkerWorldViz(TextVisualization):