#42 run baselines in ci

torch_training/bla.py

@@ -148,46 +148,46 @@ def main(argv):
                     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
+        #     # 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)