minor changes to baselines (#243)

* minor changes to baselines

* fix spaces reference

* remove flake8 disable comments and fix import

* okay maybe don't add spec to vec_env

baselines/bench/benchmarks.py

@@ -20,7 +20,7 @@ def register_benchmark(benchmark):
     if 'tasks' in benchmark:
         for t in benchmark['tasks']:
             if 'desc' not in t:
-                t['desc'] = remove_version_re.sub('', t['env_id'])
+                t['desc'] = remove_version_re.sub('', t.get('env_id', t.get('id')))
     _BENCHMARKS.append(benchmark)
 
 

baselines/common/atari_wrappers.py

@@ -6,6 +6,8 @@ import gym
 from gym import spaces
 import cv2
 cv2.ocl.setUseOpenCL(False)
+from .wrappers import TimeLimit
+
 
 class NoopResetEnv(gym.Wrapper):
     def __init__(self, env, noop_max=30):
@@ -221,11 +223,13 @@ class LazyFrames(object):
     def __getitem__(self, i):
         return self._force()[i]
 
-def make_atari(env_id):
+def make_atari(env_id, max_episode_steps=None):
     env = gym.make(env_id)
     assert 'NoFrameskip' in env.spec.id
     env = NoopResetEnv(env, noop_max=30)
     env = MaxAndSkipEnv(env, skip=4)
+    if max_episode_steps is not None:
+        env = TimeLimit(env, max_episode_steps=max_episode_steps)
     return env
 
 def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):

baselines/common/retro_wrappers.py

@@ -1,25 +1,11 @@
- # flake8: noqa F403, F405
-from .atari_wrappers import *
+from collections import deque
+import cv2
+cv2.ocl.setUseOpenCL(False)
+from .atari_wrappers import WarpFrame, ClipRewardEnv, FrameStack, ScaledFloatFrame
+from .wrappers import TimeLimit
 import numpy as np
 import gym
 
-class TimeLimit(gym.Wrapper):
-    def __init__(self, env, max_episode_steps=None):
-        super(TimeLimit, self).__init__(env)
-        self._max_episode_steps = max_episode_steps
-        self._elapsed_steps = 0
-
-    def step(self, ac):
-        observation, reward, done, info = self.env.step(ac)
-        self._elapsed_steps += 1
-        if self._elapsed_steps >= self._max_episode_steps:
-            done = True
-            info['TimeLimit.truncated'] = True
-        return observation, reward, done, info
-
-    def reset(self, **kwargs):
-        self._elapsed_steps = 0
-        return self.env.reset(**kwargs)
 
 class StochasticFrameSkip(gym.Wrapper):
     def __init__(self, env, n, stickprob):
@@ -99,7 +85,7 @@ class Downsample(gym.ObservationWrapper):
         gym.ObservationWrapper.__init__(self, env)
         (oldh, oldw, oldc) = env.observation_space.shape
         newshape = (oldh//ratio, oldw//ratio, oldc)
-        self.observation_space = spaces.Box(low=0, high=255,
+        self.observation_space = gym.spaces.Box(low=0, high=255,
             shape=newshape, dtype=np.uint8)
 
     def observation(self, frame):
@@ -116,7 +102,7 @@ class Rgb2gray(gym.ObservationWrapper):
         """
         gym.ObservationWrapper.__init__(self, env)
         (oldh, oldw, _oldc) = env.observation_space.shape
-        self.observation_space = spaces.Box(low=0, high=255,
+        self.observation_space = gym.spaces.Box(low=0, high=255,
             shape=(oldh, oldw, 1), dtype=np.uint8)
 
     def observation(self, frame):
@@ -213,8 +199,10 @@ class StartDoingRandomActionsWrapper(gym.Wrapper):
                 self.some_random_steps()
         return self.last_obs, rew, done, info
 
-def make_retro(*, game, state, max_episode_steps, **kwargs):
+def make_retro(*, game, state=None, max_episode_steps=4500, **kwargs):
     import retro
+    if state is None:
+        state = retro.State.DEFAULT
     env = retro.make(game, state, **kwargs)
     env = StochasticFrameSkip(env, n=4, stickprob=0.25)
     if max_episode_steps is not None:

baselines/common/tf_util.py

@@ -1,4 +1,3 @@
-import joblib
 import numpy as np
 import tensorflow as tf  # pylint: ignore-module
 import copy
@@ -336,6 +335,7 @@ def save_state(fname, sess=None):
 # TODO: ensure there is no subtle differences and remove one
 
 def save_variables(save_path, variables=None, sess=None):
+    import joblib
     sess = sess or get_session()
     variables = variables or tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
 
@@ -347,6 +347,7 @@ def save_variables(save_path, variables=None, sess=None):
     joblib.dump(save_dict, save_path)
 
 def load_variables(load_path, variables=None, sess=None):
+    import joblib
     sess = sess or get_session()
     variables = variables or tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
 

baselines/common/wrappers.py

@@ -0,0 +1,19 @@
+import gym
+
+class TimeLimit(gym.Wrapper):
+    def __init__(self, env, max_episode_steps=None):
+        super(TimeLimit, self).__init__(env)
+        self._max_episode_steps = max_episode_steps
+        self._elapsed_steps = 0
+
+    def step(self, ac):
+        observation, reward, done, info = self.env.step(ac)
+        self._elapsed_steps += 1
+        if self._elapsed_steps >= self._max_episode_steps:
+            done = True
+            info['TimeLimit.truncated'] = True
+        return observation, reward, done, info
+
+    def reset(self, **kwargs):
+        self._elapsed_steps = 0
+        return self.env.reset(**kwargs)