mem maze env ok 1.2

configs.yaml

@@ -156,8 +156,10 @@ debug:
   batch_size: 10
   batch_length: 20
 
-mazegym:
+MemoryMaze:
-  task: '9'
+  actor_dist: 'onehot'
+  imag_gradient: 'reinforce'
+  task: '9x9'
   steps: 1e6
   action_repeat: 2
 

dreamer.py

@@ -210,17 +210,17 @@ 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 == "mazegym":
+    elif suite == "MemoryMaze":
         import gym
-        if task == '9':
+        if task == '9x9':
             env = gym.make('memory_maze:MemoryMaze-9x9-v0')
-        elif task == '15':
+        elif task == '15x15':
             env = gym.make('memory_maze:MemoryMaze-15x15-v0')
         else:
             raise NotImplementedError(suite)
-        from envs.memmazeEnv import MZGymWrapper
+        from envs.memorymaze import MemoryMaze
-        env = MZGymWrapper(env)
+        env = MemoryMaze(env)
-        env = wrappers.OneHotAction2(env)
+        env = wrappers.OneHotAction(env)
     else:
         raise NotImplementedError(suite)
     env = wrappers.TimeLimit(env, config.time_limit)

envs/memorymaze.py

@@ -1,8 +1,6 @@
 import atexit
 import os
 import sys
-import threading
-import traceback
 
 import cloudpickle
 import gym
@@ -10,7 +8,7 @@ import numpy as np
 
 ###from tf dreamerv2 code
 
-class MZGymWrapper:
+class MemoryMaze:
 
   def __init__(self, env, obs_key='image', act_key='action', size=(64, 64)):
     self._env = env
@@ -74,7 +72,7 @@ class MZGymWrapper:
     # obs['reward'] = float(reward)
     obs['is_first'] = False
     obs['is_last'] = done
-    obs['is_terminal'] = info.get('is_terminal', done)
+    obs['is_terminal'] = info.get('is_terminal', False)
     return obs, reward, done, info
 
   def reset(self):

envs/wrappers.py

@@ -77,8 +77,8 @@ class CollectDataset:
             dtype = {16: np.int16, 32: np.int32, 64: np.int64}[self._precision]
         elif np.issubdtype(value.dtype, np.uint8):
             dtype = np.uint8
-        elif np.issubdtype(value.dtype, np.bool_):
+        elif np.issubdtype(value.dtype, np.bool):
-            dtype = np.bool_
+            dtype = np.bool
         else:
             raise NotImplementedError(value.dtype)
         return value.astype(dtype)
@@ -168,40 +168,6 @@ class OneHotAction:
         reference[index] = 1.0
         return reference
 
-class OneHotAction2:
-    def __init__(self, env):
-        assert isinstance(env.action_space, gym.spaces.Discrete)
-        self._env = env
-        self._random = np.random.RandomState()
-
-    def __getattr__(self, name):
-        return getattr(self._env, name)
-
-    @property
-    def action_space(self):
-        shape = (self._env.action_space.n,)
-        space = gym.spaces.Box(low=0, high=1, shape=shape, dtype=np.float32)
-        space.sample = self._sample_action
-        space.discrete = True
-        return space
-
-    def step(self, action):
-        index = np.argmax(action).astype(int)
-        reference = np.zeros_like(action)
-        reference[index] = 1
-        # if not np.allclose(reference, action):
-        #     raise ValueError(f"Invalid one-hot action:\n{action}")
-        return self._env.step(index)
-
-    def reset(self):
-        return self._env.reset()
-
-    def _sample_action(self):
-        actions = self._env.action_space.n
-        index = self._random.randint(0, actions)
-        reference = np.zeros(actions, dtype=np.float32)
-        reference[index] = 1.0
-        return reference
 
 class RewardObs:
     def __init__(self, env):

tools.py

@@ -127,7 +127,7 @@ def simulate(agent, envs, steps=0, episodes=0, state=None):
     # Initialize or unpack simulation state.
     if state is None:
         step, episode = 0, 0
-        done = np.ones(len(envs), np.bool_)
+        done = np.ones(len(envs), np.bool)
         length = np.zeros(len(envs), np.int32)
         obs = [None] * len(envs)
         agent_state = None