env v0.13

configs.yaml

@@ -25,7 +25,7 @@ defaults:
   action_repeat: 2
   time_limit: 1000
   grayscale: False
-  prefill: 250 #0
+  prefill: 3500
   eval_noise: 0.0
   reward_EMA: True
 
@@ -150,7 +150,7 @@ atari100k:
 
 mazed:
   task: "memmaze_9_9"
-  steps: 5e4
+  steps: 1e6
   action_repeat: 2
 
 debug:
@@ -162,12 +162,12 @@ debug:
 
 mazegym:
   #task: "memory_maze:MemoryMaze-9x9-v0"
-  steps: 5e4
+  steps: 1e6
   action_repeat: 2
 
 mazedeepm:
   task: "memmaze_9_9"
-  steps: 5e4
+  steps: 1e6
   action_repeat: 2
 
 

envs/memmazeEnv.py

@@ -11,12 +11,14 @@ import numpy as np
 
 class MZGymWrapper:
 
-  def __init__(self, env, obs_key='image', act_key='action'):
+  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('__'):

envs/wrappers.py

@@ -155,8 +155,8 @@ class OneHotAction:
         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}")
+        # if not np.allclose(reference, action):
+        #     raise ValueError(f"Invalid one-hot action:\n{action}")
         return self._env.step(index)
 
     def reset(self):

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