modified memory maze and dependencies

dreamer.py

@@ -212,21 +212,11 @@ def make_env(config, logger, mode, train_eps, eval_eps):
         )
         env = wrappers.OneHotAction(env)
     elif suite == "MemoryMaze":
-        import gym
-
-        if task == "9x9":
-            env = gym.make("memory_maze:MemoryMaze-9x9-v0")
-        elif task == "15x15":
-            env = gym.make("memory_maze:MemoryMaze-15x15-v0")
-        else:
-            raise NotImplementedError(suite)
         from envs.memorymaze import MemoryMaze
-
-        env = MemoryMaze(env)
+        env = MemoryMaze(task)
         env = wrappers.OneHotAction(env)
     elif suite == "crafter":
         import envs.crafter as crafter
-
         env = crafter.Crafter(task, config.size)
         env = wrappers.OneHotAction(env)
     else:

envs/memorymaze.py

@@ -5,10 +5,14 @@ import numpy as np
 
 
 class MemoryMaze:
-    def __init__(self, env, obs_key="image", act_key="action", size=(64, 64)):
-        self._env = env
+    def __init__(self, task, obs_key="image", act_key="action", size=(64, 64)):
+        if task == "9x9":
+            self._env = gym.make("memory_maze:MemoryMaze-9x9-v0")
+        elif task == "15x15":
+            self._env = gym.make("memory_maze:MemoryMaze-15x15-v0")
+        else:
+            raise NotImplementedError(task)
         self._obs_is_dict = hasattr(self._env.observation_space, "spaces")
-        self._act_is_dict = hasattr(self._env.action_space, "spaces")
         self._obs_key = obs_key
         self._act_key = act_key
         self._size = size
@@ -23,35 +27,18 @@ class MemoryMaze:
             raise ValueError(name)
 
     @property
-    def obs_space(self):
+    def observation_space(self):
         if self._obs_is_dict:
             spaces = self._env.observation_space.spaces.copy()
         else:
             spaces = {self._obs_key: self._env.observation_space}
-        return {
+        return gym.spaces.Dict({
             **spaces,
             "reward": gym.spaces.Box(-np.inf, np.inf, (), dtype=np.float32),
             "is_first": gym.spaces.Box(0, 1, (), dtype=bool),
             "is_last": gym.spaces.Box(0, 1, (), dtype=bool),
             "is_terminal": gym.spaces.Box(0, 1, (), dtype=bool),
-        }
-
-    @property
-    def act_space(self):
-        if self._act_is_dict:
-            return self._env.action_space.spaces.copy()
-        else:
-            return {self._act_key: self._env.action_space}
-
-    @property
-    def observation_space(self):
-        img_shape = self._size + ((1,) if self._gray else (3,))
-        return gym.spaces.Dict(
-            {
-                "image": gym.spaces.Box(0, 255, img_shape, np.uint8),
-            }
-        )
-
+        })
     @property
     def action_space(self):
         space = self._env.action_space
@@ -59,12 +46,10 @@ class MemoryMaze:
         return space
 
     def step(self, action):
-        # if not self._act_is_dict:
-        #   action = action[self._act_key]
         obs, reward, done, info = self._env.step(action)
         if not self._obs_is_dict:
             obs = {self._obs_key: obs}
-        # obs['reward'] = float(reward)
+        obs['reward'] = reward
         obs["is_first"] = False
         obs["is_last"] = done
         obs["is_terminal"] = info.get("is_terminal", False)

requirements.txt

@@ -1,13 +1,15 @@
+setuptools==60.0.0
 torch==2.0.0
 torchvision==0.15.1
 numpy==1.20.1
 tensorboard==2.5.0
 pandas==1.2.4
-matplotlib==3.4.1
+matplotlib==3.5.0
 ruamel.yaml==0.17.4
 moviepy==1.0.3
 einops==0.3.0
 protobuf==3.20.0
 gym==0.19.0
 dm_control==1.0.9
-scipy==scipy==1.9.0
+scipy==1.7.0
+memory_maze