Importing Training module

train.py

@@ -26,13 +26,16 @@ def parse_args():
     parser.add_argument('--image_size', default=84, type=int)
     parser.add_argument('--action_repeat', default=1, type=int)
     parser.add_argument('--frame_stack', default=3, type=int)
+    parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none'])
+    parser.add_argument('--resource_files', type=str)
+    parser.add_argument('--total_frames', default=10000, type=int)
     # replay buffer
     parser.add_argument('--replay_buffer_capacity', default=1000000, type=int)
     # train
     parser.add_argument('--agent', default='sac_ae', type=str)
     parser.add_argument('--init_steps', default=1000, type=int)
     parser.add_argument('--num_train_steps', default=1000000, type=int)
-    parser.add_argument('--batch_size', default=128, type=int)
+    parser.add_argument('--batch_size', default=512, type=int)
     parser.add_argument('--hidden_dim', default=1024, type=int)
     # eval
     parser.add_argument('--eval_freq', default=10000, type=int)
@@ -143,7 +146,10 @@ def main():
         from_pixels=(args.encoder_type == 'pixel'),
         height=args.image_size,
         width=args.image_size,
-        frame_skip=args.action_repeat
+        frame_skip=args.action_repeat,
+        img_source=args.img_source,
+        resource_files=args.resource_files,
+        total_frames=args.total_frames
     )
     env.seed(args.seed)
 
@@ -212,28 +218,65 @@ def main():
 
             L.log('train/episode', episode, step)
 
+        if episode_step == 0:
+            last_obs = obs
+            # sample action for data collection
+            if step < args.init_steps:
+                last_action = env.action_space.sample()
+            else:
+                with utils.eval_mode(agent):
+                    last_action = agent.sample_action(last_obs)
+            
+            curr_obs, last_reward, last_done, _ = env.step(last_action)
+
+            # allow infinit bootstrap
+            last_done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(last_done)
+            episode_reward += last_reward
+
+            # sample action for data collection
+            if step < args.init_steps:
+                action = env.action_space.sample()
+            else:
+                with utils.eval_mode(agent):
+                    action = agent.sample_action(curr_obs)
+            
+            next_obs, reward, done, _ = env.step(action)
+
+            # allow infinit bootstrap
+            done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(done)
+            episode_reward += reward
+
+            replay_buffer.add(last_obs, last_action, last_reward, curr_obs, last_done_bool, action, reward, next_obs, done_bool)
+
+        last_obs = curr_obs
+        last_action = action
+        last_reward = reward
+        last_done = done
+        curr_obs = next_obs
+
         # sample action for data collection
         if step < args.init_steps:
             action = env.action_space.sample()
         else:
             with utils.eval_mode(agent):
-                action = agent.sample_action(obs)
+                action = agent.sample_action(curr_obs)
 
+        
         # run training update
         if step >= args.init_steps:
-            num_updates = args.init_steps if step == args.init_steps else 1
+            #num_updates = args.init_steps if step == args.init_steps else 1
+            num_updates = 1 if step == args.init_steps else 1
             for _ in range(num_updates):
                 agent.update(replay_buffer, L, step)
 
         next_obs, reward, done, _ = env.step(action)
 
         # allow infinit bootstrap
-        done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(
+        done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(done)
-            done
-        )
         episode_reward += reward
 
-        replay_buffer.add(obs, action, reward, next_obs, done_bool)
+        #replay_buffer.add(obs, action, reward, next_obs, done_bool)
+        replay_buffer.add(last_obs, last_action, last_reward, curr_obs, last_done_bool, action, reward, next_obs, done_bool)
 
         obs = next_obs
         episode_step += 1