align terminology of BPD with experiment scripts

momaland/learning/iql/tabular_bpd.py

@@ -65,9 +65,9 @@ def normalize_objective_rewards(self, reward, reward_scheme):
             np.array: the normalized reward
         """
         # Set the normalization constants
-        if reward_scheme == "local":
+        if reward_scheme == "individual":
             cap_min, cap_max, mix_min, mix_max = self.l_cap_min, self.l_cap_max, self.l_mix_min, self.l_mix_max
-        elif reward_scheme == "global":
+        elif reward_scheme == "team":
             cap_min, cap_max, mix_min, mix_max = self.g_cap_min, self.g_cap_max, self.g_mix_min, self.g_mix_max
         else:
             raise ValueError(f"Unknown reward scheme: {reward_scheme}")
@@ -108,15 +108,15 @@ def step(self, actions):
             section_agent_types[self._state[i]][self._types[i]] += 1
         g_capacity = _global_capacity_reward(self.resource_capacities, section_consumptions)
         g_mixture = _global_mixture_reward(section_agent_types)
-        g_capacity_norm, g_mixture_norm = self.normalize_objective_rewards(np.array([g_capacity, g_mixture]), "global")
+        g_capacity_norm, g_mixture_norm = self.normalize_objective_rewards(np.array([g_capacity, g_mixture]), "team")
         infos = {
             agent: {"g_cap": g_capacity, "g_mix": g_mixture, "g_cap_norm": g_capacity_norm, "g_mix_norm": g_mixture_norm}
             for agent in self.possible_agents
         }
 
         # Normalize the rewards
         for agent in self.possible_agents:
-            rewards[agent] = self.normalize_objective_rewards(rewards[agent], self.reward_scheme)
+            rewards[agent] = self.normalize_objective_rewards(rewards[agent], self.reward_mode)
 
         return observations, rewards, terminations, truncations, infos
 

momaland/learning/iql/train_iql_bpd.py

@@ -38,7 +38,7 @@ def compute_normalization_constants(num_agents, sections, capacity, type_distrib
     # Maximum local capacity is achieved when there are 'capacity' agents in the section
     max_cap_local = _local_capacity_reward(capacity, capacity)
     cap_min = 0.0
-    cap_max = max_cap_local if reward_scheme == "local" else max_cap_global
+    cap_max = max_cap_local if reward_scheme == "individual" else max_cap_global
 
     #   Mixture
     # Maximum global mixture: one agent of each type in each section, except one where all other agents are
@@ -52,7 +52,7 @@ def compute_normalization_constants(num_agents, sections, capacity, type_distrib
     # Maximum local mixture is achieved when there is one agent of each type in the section
     max_mix_local = _local_mixture_reward([1, 1])
     mix_min = 0.0
-    mix_max = max_mix_local if reward_scheme == "local" else max_mix_global
+    mix_max = max_mix_local if reward_scheme == "individual" else max_mix_global
 
     return cap_min, cap_max, mix_min, mix_max
 
@@ -94,7 +94,7 @@ def parse_args():
     parser.add_argument('--position-distribution', type=float, nargs=5, default=[0., 0.5, 0., 0.5, 0.], )
     parser.add_argument('--sections', type=int, default=5, )
     parser.add_argument('--capacity', type=int, default=3, )
-    parser.add_argument('--reward-scheme', type=str, default="local", help="the reward scheme to use")
+    parser.add_argument('--reward-scheme', type=str, default="individual", help="the reward scheme to use")
 
     args = parser.parse_args()
     args.time = time.time()
@@ -112,13 +112,13 @@ def parse_args():
         "position_distribution": args.position_distribution,
         "sections": args.sections,
         "capacity": args.capacity,
-        "reward_scheme": args.reward_scheme,
+        "reward_mode": args.reward_scheme,
         # Normalization constants
         "local_constants": compute_normalization_constants(
-            args.num_agents, args.sections, args.capacity, args.type_distribution, "local"
+            args.num_agents, args.sections, args.capacity, args.type_distribution, "individual"
         ),
         "global_constants": compute_normalization_constants(
-            args.num_agents, args.sections, args.capacity, args.type_distribution, "global"
+            args.num_agents, args.sections, args.capacity, args.type_distribution, "team"
         ),
     }