Merge pull request #21 from zdx3578/memmaze

Mem maze env

configs.yaml

@@ -172,9 +172,21 @@ atari100k:
   imag_gradient: 'reinforce'
   time_limit: 108000
 
+
 debug:
   debug: True
   pretrain: 1
   prefill: 1
   batch_size: 10
   batch_length: 20
+
+MemoryMaze:
+  actor_dist: 'onehot'
+  imag_gradient: 'reinforce'
+  task: '9x9'
+  steps: 1e6
+  action_repeat: 2
+
+
+
+

dreamer.py

@@ -211,6 +211,17 @@ def make_env(config, logger, mode, train_eps, eval_eps):
             task, mode if "train" in mode else "test", config.action_repeat
         )
         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 = wrappers.OneHotAction(env)
     elif suite == "crafter":
         import envs.crafter as crafter
 

envs/memorymaze.py

@@ -0,0 +1,87 @@
+import atexit
+import os
+import sys
+
+import cloudpickle
+import gym
+import numpy as np
+
+###from tf dreamerv2 code
+
+class MemoryMaze:
+
+  def __init__(self, env, obs_key='image', act_key='action', size=(64, 64)):
+    self._env = env
+    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
+    self._gray = False
+
+  def __getattr__(self, name):
+    if name.startswith('__'):
+      raise AttributeError(name)
+    try:
+      return getattr(self._env, name)
+    except AttributeError:
+      raise ValueError(name)
+
+  @property
+  def obs_space(self):
+    if self._obs_is_dict:
+      spaces = self._env.observation_space.spaces.copy()
+    else:
+      spaces = {self._obs_key: self._env.observation_space}
+    return {
+        **spaces,
+        'reward': gym.spaces.Box(-np.inf, np.inf, (), dtype=np.float32),
+        'is_first': gym.spaces.Box(0, 1, (), dtype=np.bool),
+        'is_last': gym.spaces.Box(0, 1, (), dtype=np.bool),
+        'is_terminal': gym.spaces.Box(0, 1, (), dtype=np.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
+    space.discrete = True
+    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['is_first'] = False
+    obs['is_last'] = done
+    obs['is_terminal'] = info.get('is_terminal', False)
+    return obs, reward, done, info
+
+  def reset(self):
+    obs = self._env.reset()
+    if not self._obs_is_dict:
+      obs = {self._obs_key: obs}
+    obs['reward'] = 0.0
+    obs['is_first'] = True
+    obs['is_last'] = False
+    obs['is_terminal'] = False
+    return obs
+