added rendering trained agent

networks/utils.py

@@ -27,7 +27,7 @@ def initialize_modules(model, nonlinearity='leaky_relu', init_type='kaiming'):
 
 def load_weights(state_dict_path, models, model_names, optimizers=[], optimizer_names=[], return_val=None, return_vals=None):
     def put_in_list(item):
-        if not isinstance(item, list, tuple) and item is not None:
+        if not isinstance(item, (list, tuple)) and item is not None:
             item = [item]
         return item
 

td3/main.py

@@ -79,12 +79,13 @@
         score_history.append(score)
         moving_avg = sum(score_history) / len(score_history)
         agent.add_scalar('Average Score', moving_avg, global_step=e)
-        tqdm.write(f'Episode: {e + 1}/{args.n_episodes}, \
-                Episode Score: {score}, \
-                Average Score: {moving_avg}, \
-                Best Score: {best_score}')
 
         # save weights @ best score
         if moving_avg > best_score:
             best_score = moving_avg
             agent.save_networks()
+
+        tqdm.write(f'Episode: {e + 1}/{args.n_episodes}, \
+                Episode Score: {score}, \
+                Average Score: {moving_avg}, \
+                Best Score: {best_score}')

td3/render.py

@@ -0,0 +1,39 @@
+import gym
+from tqdm import tqdm
+
+from td3.agent import Agent
+from td3.main import args
+from networks.utils import load_weights
+
+
+if __name__ == '__main__':
+    args.checkpoint_dir += f'/{args.env_name}_td3.pth'
+    # env & agent
+    env = gym.make(args.env_name)
+    agent = Agent(env, args.alpha, args.beta, args.hidden_dims, args.tau, args.batch_size,
+                  args.gamma, args.d, 0, args.max_size, args.c, args.sigma,
+                  args.one_device, args.log_dir, args.checkpoint_dir)
+    best_score = env.reward_range[0]
+    load_weights(args.checkpoint_dir,
+                 [agent.actor] , ['actor'])
+    episodes = tqdm(range(args.n_episodes))
+    for e in episodes:
+        # resetting
+        state = env.reset()
+        done = False
+        score = 0
+
+        while not done:
+            action = agent.choose_action(state)
+            state_, reward, done, _ = env.step(action)
+
+            # reset, log & render
+            score += reward
+            state = state_
+            episodes.set_postfix({'Reward': reward})
+            env.render()
+        if score > best_score:
+            best_score = score
+        tqdm.write(f'Episode: {e + 1}/{args.n_episodes}, \
+                    Episode Score: {score}, \
+                    Best Score: {best_score}')