Temp push

textworld/challenges/spaceship/agent_design_a2c.py

@@ -265,7 +265,7 @@ def _discount_rewards(self, last_values):
 
 def play(agent, path, max_step=50, nb_episodes=10, verbose=True):
     """
-        This code uses the cooking agent design in the spaceship game.
+        This code uses the agent design in the spaceship game.
 
         :param agent: the obj of NeuralAgent, a sample object for the agent
         :param path: The path to the game (envo model)
@@ -302,9 +302,13 @@ def play(agent, path, max_step=50, nb_episodes=10, verbose=True):
         nb_moves = 0
         while not done:
             command = agent.act(obs, score, done, infos)
+            print(command, "....", end="")
             obs, score, done, infos = env.step(command)
             nb_moves += 1
         agent.act(obs, score, done, infos)  # Let the agent know the game is done.
+        print(score)
+        print(obs)
+        print('-------------------------------------')
 
         if verbose:
             print(".", end="")

textworld/challenges/spaceship/build_agent_TW_tutorial.py

@@ -82,8 +82,8 @@ class NeuralAgent:
     GAMMA = 0.9
 
     def __init__(self) -> None:
-        self._initialized = False
-        self._epsiode_has_started = False
+        # self._initialized = False
+        # self._epsiode_has_started = False
         self.id2word = ["<PAD>", "<UNK>"]
         self.word2id = {w: i for i, w in enumerate(self.id2word)}
 
@@ -138,6 +138,7 @@ def act(self, obs: str, score: int, done: bool, infos: Mapping[str, Any]) -> Opt
             self.transitions[-1][0] = reward  # Update reward information.
 
         self.stats["max"]["score"].append(score)
+
         if self.no_train_step % self.UPDATE_FREQUENCY == 0:
             # Update model
             returns, advantages = self._discount_rewards(values)
@@ -252,10 +253,12 @@ def play(agent, path, max_step=50, nb_episodes=10, verbose=True):
             print(os.path.basename(path), end="")
 
     # Collect some statistics: nb_steps, final reward.
-    avg_moves, avg_scores, avg_norm_scores, seed_h = [], [], [], None
+    avg_moves, avg_scores, avg_norm_scores, seed_h = [], [], [], 4567
     for no_episode in range(nb_episodes):
         obs, infos = env.reset()  # Start new episode.
-        seed_h = env.env.textworld_env._wrapped_env.seed(init_seed=seed_h)
+
+        env.env.textworld_env._wrapped_env.seed(seed=seed_h)
+        seed_h += 1
 
         score = 0
         done = False
@@ -288,7 +291,8 @@ def play(agent, path, max_step=50, nb_episodes=10, verbose=True):
 agent.train()  # Tell the agent it should update its parameters.
 
 starttime = time()
-play(agent, "./games/levelMedium.ulx", nb_episodes=500, verbose=False)  # Medium level game.
+print(os.path.realpath("./games/levelMedium.ulx"))
+play(agent, "./games/levelMedium.ulx", nb_episodes=25, verbose=False)  # Medium level game.
 print("Trained in {:.2f} secs".format(time() - starttime))
 
 print('==============   Time To Test   ============== ')