rename te, tr to tm, tc

metadrive/envs/marl_envs/marl_tollgate.py

@@ -273,9 +273,9 @@ def get_single_observation(self, vehicle_config):
         return o
 
     def step(self, actions):
-        o, r, te, tr, i = super(MultiAgentTollgateEnv, self).step(actions)
+        o, r, tm, tc, i = super(MultiAgentTollgateEnv, self).step(actions)
         self.stay_time_manager.record(self.agent_manager.active_agents, self.episode_step)
-        return o, r, te, tr, i
+        return o, r, tm, tc, i
 
     def setup_engine(self):
         super(MultiAgentTollgateEnv, self).setup_engine()
@@ -317,13 +317,13 @@ def _expert():
     total_r = 0
     ep_s = 0
     for i in range(1, 100000):
-        o, r, te, tr, info = env.step(env.action_space.sample())
+        o, r, tm, tc, info = env.step(env.action_space.sample())
         for r_ in r.values():
             total_r += r_
         ep_s += 1
-        te.update({"total_r": total_r, "episode length": ep_s})
+        tm.update({"total_r": total_r, "episode length": ep_s})
         # env.render(text=d)
-        if te["__all__"]:
+        if tm["__all__"]:
             print(
                 "Finish! Current step {}. Group Reward: {}. Average reward: {}".format(
                     i, total_r, total_r / env.agent_manager.next_agent_count
@@ -361,7 +361,7 @@ def _vis_debug_respawn():
     ep_s = 0
     for i in range(1, 100000):
         action = {k: [.0, 1.0] for k in env.vehicles.keys()}
-        o, r, te, tr, info = env.step(action)
+        o, r, tm, tc, info = env.step(action)
         for r_ in r.values():
             total_r += r_
         ep_s += 1
@@ -374,7 +374,7 @@ def _vis_debug_respawn():
             "cam_z": env.main_camera.top_down_camera_height
         }
         env.render(text=render_text)
-        if te["__all__"]:
+        if tm["__all__"]:
             print(
                 "Finish! Current step {}. Group Reward: {}. Average reward: {}".format(
                     i, total_r, total_r / env.agent_manager.next_agent_count
@@ -415,7 +415,7 @@ def _vis():
     total_r = 0
     ep_s = 0
     for i in range(1, 100000):
-        o, r, te, tr, info = env.step({k: [0, 1] for k in env.vehicles.keys()})
+        o, r, tm, tc, info = env.step({k: [0, 1] for k in env.vehicles.keys()})
         for r_ in r.values():
             total_r += r_
         ep_s += 1
@@ -436,7 +436,7 @@ def _vis():
         render_text["lane"] = env.current_track_vehicle.lane_index
         render_text["block"] = env.current_track_vehicle.navigation.current_road.block_ID()
         env.render(text=render_text)
-        if te["__all__"]:
+        if tm["__all__"]:
             print(info)
             print(
                 "Finish! Current step {}. Group Reward: {}. Average reward: {}".format(
@@ -457,12 +457,12 @@ def _profile():
     obs, _ = env.reset()
     start = time.time()
     for s in range(10000):
-        o, r, te, tr, i = env.step(env.action_space.sample())
+        o, r, tm, tc, i = env.step(env.action_space.sample())
 
         # mask_ratio = env.engine.detector_mask.get_mask_ratio()
         # print("Mask ratio: ", mask_ratio)
 
-        if all(te.values()):
+        if all(tm.values()):
             env.reset()
         if (s + 1) % 100 == 0:
             print(
@@ -471,7 +471,7 @@ def _profile():
                     time.time() - start, (s + 1) / (time.time() - start)
                 )
             )
-    print(f"(MetaDriveEnv) Total Time Elapse: {time.time() - start}")
+    print(f"(MetaDriveEnv) Total Time Elapsed: {time.time() - start}")
 
 
 def _long_run():
@@ -500,30 +500,30 @@ def _long_run():
         assert env.observation_space.contains(obs)
         for step in range(10000):
             act = env.action_space.sample()
-            o, r, te, tr, i = env.step(act)
+            o, r, tm, tc, i = env.step(act)
             if step == 0:
-                assert not any(te.values())
+                assert not any(tm.values())
 
-            if any(te.values()):
-                print("Current Done: {}\nReward: {}".format(te, r))
-                for kkk, ddd in te.items():
+            if any(tm.values()):
+                print("Current Done: {}\nReward: {}".format(tm, r))
+                for kkk, ddd in tm.items():
                     if ddd and kkk != "__all__":
                         print("Info {}: {}\n".format(kkk, i[kkk]))
                 print("\n")
 
             for kkk, rrr in r.items():
                 if rrr == -_out_of_road_penalty:
-                    assert te[kkk]
+                    assert tm[kkk]
 
             if (step + 1) % 200 == 0:
                 print(
                     "{}/{} Agents: {} {}\nO: {}\nR: {}\nD: {}\nI: {}\n\n".format(
                         step + 1, 10000, len(env.vehicles), list(env.vehicles.keys()),
                         {k: (oo.shape, oo.mean(), oo.min(), oo.max())
-                         for k, oo in o.items()}, r, te, i
+                         for k, oo in o.items()}, r, tm, i
                     )
                 )
-            if te["__all__"]:
+            if tm["__all__"]:
                 print('Current step: ', step)
                 break
     finally:

metadrive/envs/marl_envs/tinyinter.py

@@ -367,13 +367,13 @@ def _get_reset_return(self):
         return self.agent_manager.filter_RL_agents(org)
 
     def step(self, actions):
-        o, r, te, tr, i = super(MultiAgentTinyInter, self).step(actions)
+        o, r, tm, tc, i = super(MultiAgentTinyInter, self).step(actions)
 
         # if self.num_RL_agents == self.num_agents:
-        #     return o, r, te, tr, i
+        #     return o, r, tm, tc, i
 
-        original_done_dict = copy.deepcopy(te)
-        d = self.agent_manager.filter_RL_agents(te, original_done_dict=original_done_dict)
+        original_done_dict = copy.deepcopy(tm)
+        d = self.agent_manager.filter_RL_agents(tm, original_done_dict=original_done_dict)
         if "__all__" in d:
             d.pop("__all__")
         # assert len(d) == self.agent_manager.num_RL_agents, d
@@ -382,7 +382,7 @@ def step(self, actions):
             self.agent_manager.filter_RL_agents(o, original_done_dict=original_done_dict),
             self.agent_manager.filter_RL_agents(r, original_done_dict=original_done_dict),
             d,
-            self.agent_manager.filter_RL_agents(tr, original_done_dict=original_done_dict),
+            self.agent_manager.filter_RL_agents(tc, original_done_dict=original_done_dict),
             self.agent_manager.filter_RL_agents(i, original_done_dict=original_done_dict),
         )
 
@@ -445,11 +445,11 @@ def get_single_observation(self, vehicle_config: "Config"):
     ep_reward_sum = 0.0
     ep_success_reward_sum = 0.0
     for i in range(1, 100000):
-        o, r, te, tr, info = env.step({k: [0.0, 1.0] for k in env.action_space.sample().keys()})
+        o, r, tm, tc, info = env.step({k: [0.0, 1.0] for k in env.action_space.sample().keys()})
         # env.render("top_down", camera_position=(42.5, 0), film_size=(500, 500))
         vehicles = env.vehicles
 
-        for k, v in te.items():
+        for k, v in tm.items():
             if v and k in info:
                 ep_success += int(info[k]["arrive_dest"])
                 ep_reward_sum += int(info[k]["episode_reward"])
@@ -461,10 +461,10 @@ def get_single_observation(self, vehicle_config: "Config"):
         #     assert sum(
         #         [env.engine.get_policy(v.name).__class__.__name__ == "EnvInputPolicy" for k, v in vehicles.items()]
         #     ) == env.config["num_RL_agents"]
-        if any(te.values()):
-            print("Somebody dead.", te, info)
+        if any(tm.values()):
+            print("Somebody dead.", tm, info)
             # print("Step {}. Policies: {}".format(i, {k: v['policy'] for k, v in info.items()}))
-        if te["__all__"]:
+        if tm["__all__"]:
             # assert i >= 1000
             print("Reset. ", i, info)
             # break

metadrive/envs/real_data_envs/waymo_env.py

@@ -62,7 +62,7 @@ def _merge_extra_config(self, config):
             env.reset(force_seed=i)
             while True:
                 step_start = time.time()
-                o, r, te, tr, info = env.step([0, 0])
+                o, r, tm, tc, info = env.step([0, 0])
                 assert env.observation_space.contains(o)
                 # c_lane = env.vehicle.lane
                 # long, lat, = c_lane.local_coordinates(env.vehicle.position)
@@ -75,7 +75,7 @@ def _merge_extra_config(self, config):
                     # mode="topdown"
                 )
 
-                if te or tr:
+                if tm or tc:
                     if info["arrive_dest"]:
                         print("seed:{}, success".format(env.engine.global_random_seed))
                     break