aoc_tsp: Replace scheduler with AgentSet functionality

Also use the new, standardized order of count-do-collect.

examples/aco_tsp/aco_tsp/model.py

@@ -158,8 +158,6 @@ class AcoTspModel(mesa.Model):
 
     There is only one model-level parameter: how many agents the model contains. When a new model
     is started, we want it to populate itself with the given number of agents.
-
-    The scheduler is a special model component which controls the order in which agents are activated.
     """
 
     def __init__(
@@ -176,12 +174,10 @@ def __init__(
         self.num_cities = tsp_graph.num_cities
         self.all_cities = set(range(self.num_cities))
         self.max_steps = max_steps
-        self.schedule = mesa.time.RandomActivation(self)
         self.grid = mesa.space.NetworkGrid(tsp_graph.g)
 
         for i in range(self.num_agents):
             agent = AntTSP(unique_id=i, model=self, alpha=ant_alpha, beta=ant_beta)
-            self.schedule.add(agent)
 
             city = tsp_graph.cities[self.random.randrange(self.num_cities)]
             self.grid.place_agent(agent, city)
@@ -206,14 +202,15 @@ def __init__(
                 "tsp_solution": "tsp_solution",
             },
         )
+        self.datacollector.collect(self)  # Collect initial state at steps=0
 
         self.running = True
 
     def update_pheromone(self, q: float = 100, ro: float = 0.5):
         # tau_ij(t+1) = (1-ro)*tau_ij(t) + delta_tau_ij(t)
         # delta_tau_ij(t) = sum_k^M {Q/L^k} * I[i,j \in T^k]
         delta_tau_ij = {}
-        for k, agent in enumerate(self.schedule.agents):
+        for k, agent in enumerate(self.agents):
             delta_tau_ij[k] = agent.calculate_pheromone_delta(q)
 
         for i, j in self.grid.G.edges():
@@ -227,16 +224,14 @@ def update_pheromone(self, q: float = 100, ro: float = 0.5):
 
     def step(self):
         """
-        A model step. Used for collecting data and advancing the schedule
+        A model step. Used for activating the agents and collecting data.
         """
-        self.datacollector.collect(self)
-        self.schedule.step()
-        self.num_steps += 1
+        self.agents.shuffle().do("step")
         self.update_pheromone()
 
         # Check len of cities visited by an agent
         best_instance_iter = float("inf")
-        for agent in self.schedule.agents:
+        for agent in self.agents:
             # Check for best path
             if agent.tsp_distance < self.best_distance:
                 self.best_distance = agent.tsp_distance
@@ -249,3 +244,5 @@ def step(self):
 
         if self.num_steps >= self.max_steps:
             self.running = False
+
+        self.datacollector.collect(self)