#42 run baselines in ci

torch_training/bla.py

@@ -136,58 +136,58 @@ def main(argv):
         # env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
         for a in range(env.get_num_agents()):
             agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
-        #
-        # score = 0
-        # env_done = 0
-        # # Run episode
-        # for step in range(max_steps):
-        #     if demo:
-        #         env_renderer.renderEnv(show=True, show_observations=False)
-        #         # observation_helper.util_print_obs_subtree(obs_original[0])
-        #         if record_images:
-        #             env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(frame_step))
-        #             frame_step += 1
-        #     # print(step)
-        #     # Action
-        #     for a in range(env.get_num_agents()):
-        #         if demo:
-        #             eps = 0
-        #         # action = agent.act(np.array(obs[a]), eps=eps)
-        #         action = agent.act(agent_obs[a], eps=eps)
-        #         action_prob[action] += 1
-        #         action_dict.update({a: action})
-        #     # Environment step
-        #
-        #     next_obs, all_rewards, done, _ = env.step(action_dict)
-        #     # print(all_rewards,action)
-        #     obs_original = next_obs.copy()
-        #     for a in range(env.get_num_agents()):
-        #         data, distance, agent_data = split_tree(tree=np.array(next_obs[a]),
-        #                                                 current_depth=0)
-        #         data = norm_obs_clip(data)
-        #         distance = norm_obs_clip(distance)
-        #         agent_data = np.clip(agent_data, -1, 1)
-        #         next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
-        #     time_obs.append(next_obs)
-        #
-        #     # Update replay buffer and train agent
-        #     for a in range(env.get_num_agents()):
-        #         agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
-        #         if done[a]:
-        #             final_obs[a] = agent_obs[a].copy()
-        #             final_obs_next[a] = agent_next_obs[a].copy()
-        #             final_action_dict.update({a: action_dict[a]})
-        #         if not demo and not done[a]:
-        #             agent.step(agent_obs[a], action_dict[a], all_rewards[a], agent_next_obs[a], done[a])
-        #         score += all_rewards[a] / env.get_num_agents()
-        #
-        #     agent_obs = agent_next_obs.copy()
-        #     if done['__all__']:
-        #         env_done = 1
-        #         for a in range(env.get_num_agents()):
-        #             agent.step(final_obs[a], final_action_dict[a], all_rewards[a], final_obs_next[a], done[a])
-        #         break
-        # # Epsilon decay
+
+        score = 0
+        env_done = 0
+        # Run episode
+        for step in range(max_steps):
+            if demo:
+                env_renderer.renderEnv(show=True, show_observations=False)
+                # observation_helper.util_print_obs_subtree(obs_original[0])
+                if record_images:
+                    env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(frame_step))
+                    frame_step += 1
+            # print(step)
+            # Action
+            for a in range(env.get_num_agents()):
+                if demo:
+                    eps = 0
+                # action = agent.act(np.array(obs[a]), eps=eps)
+                action = agent.act(agent_obs[a], eps=eps)
+                action_prob[action] += 1
+                action_dict.update({a: action})
+            # Environment step
+
+            next_obs, all_rewards, done, _ = env.step(action_dict)
+            # print(all_rewards,action)
+            obs_original = next_obs.copy()
+            for a in range(env.get_num_agents()):
+                data, distance, agent_data = split_tree(tree=np.array(next_obs[a]),
+                                                        current_depth=0)
+                data = norm_obs_clip(data)
+                distance = norm_obs_clip(distance)
+                agent_data = np.clip(agent_data, -1, 1)
+                next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
+            time_obs.append(next_obs)
+
+            # Update replay buffer and train agent
+            for a in range(env.get_num_agents()):
+                agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
+                if done[a]:
+                    final_obs[a] = agent_obs[a].copy()
+                    final_obs_next[a] = agent_next_obs[a].copy()
+                    final_action_dict.update({a: action_dict[a]})
+                if not demo and not done[a]:
+                    agent.step(agent_obs[a], action_dict[a], all_rewards[a], agent_next_obs[a], done[a])
+                score += all_rewards[a] / env.get_num_agents()
+
+            agent_obs = agent_next_obs.copy()
+            if done['__all__']:
+                env_done = 1
+                for a in range(env.get_num_agents()):
+                    agent.step(final_obs[a], final_action_dict[a], all_rewards[a], final_obs_next[a], done[a])
+                break
+        # Epsilon decay
         # eps = max(eps_end, eps_decay * eps)  # decrease epsilon
         #
         # done_window.append(env_done)