change random seeding to work with new gym version (#231)

* change random seeding to work with new gym version

* move seeding to seed() method

* fix mnistenv

* actually try some of the tests before pushing

* more deterministic fixed seq

baselines/common/tests/envs/fixed_sequence_env.py

@@ -7,21 +7,20 @@ class FixedSequenceEnv(Env):
     def __init__(
             self,
             n_actions=10,
-            seed=0,
             episode_len=100
     ):
         self.np_random = np.random.RandomState()
-        self.np_random.seed(seed)
-        self.sequence = [self.np_random.randint(0, n_actions-1) for _ in range(episode_len)]
+        self.sequence = None
 
         self.action_space = Discrete(n_actions)
         self.observation_space = Discrete(1)
 
         self.episode_len = episode_len
         self.time = 0
-        self.reset()
 
     def reset(self):
+        if self.sequence is None:
+            self.sequence = [self.np_random.randint(0, self.action_space.n-1) for _ in range(self.episode_len)]
         self.time = 0
         return 0
 
@@ -35,6 +34,9 @@ class FixedSequenceEnv(Env):
 
         return 0, rew, done, {}
 
+    def seed(self, seed=None):
+        self.np_random.seed(seed)
+
     def _choose_next_state(self):
         self.time += 1
 

baselines/common/tests/envs/identity_env.py

@@ -10,6 +10,7 @@ class IdentityEnv(Env):
             episode_len=None
     ):
 
+        self.observation_space = self.action_space
         self.episode_len = episode_len
         self.time = 0
         self.reset()
@@ -17,7 +18,6 @@ class IdentityEnv(Env):
     def reset(self):
         self._choose_next_state()
         self.time = 0
-        self.observation_space = self.action_space
 
         return self.state
 
@@ -30,6 +30,9 @@ class IdentityEnv(Env):
 
         return self.state, rew, done, {}
 
+    def seed(self, seed=None):
+        self.action_space.seed(seed)
+
     def _choose_next_state(self):
         self.state = self.action_space.sample()
         self.time += 1

baselines/common/tests/envs/mnist_env.py

@@ -9,7 +9,6 @@ from gym.spaces import Discrete, Box
 class MnistEnv(Env):
     def __init__(
             self,
-            seed=0,
             episode_len=None,
             no_images=None
     ):
@@ -23,7 +22,6 @@ class MnistEnv(Env):
            self.mnist = input_data.read_data_sets(mnist_path)
 
         self.np_random = np.random.RandomState()
-        self.np_random.seed(seed)
 
         self.observation_space = Box(low=0.0, high=1.0, shape=(28,28,1))
         self.action_space = Discrete(10)
@@ -50,6 +48,9 @@ class MnistEnv(Env):
 
         return self.state[0], rew, done, {}
 
+    def seed(self, seed=None):
+        self.np_random.seed(seed)
+
     def train_mode(self):
         self.dataset = self.mnist.train
 

baselines/common/tests/test_fixed_sequence.py

@@ -33,8 +33,7 @@ def test_fixed_sequence(alg, rnn):
     kwargs = learn_kwargs[alg]
     kwargs.update(common_kwargs)
 
-    episode_len = 5
-    env_fn = lambda: FixedSequenceEnv(10, episode_len=episode_len)
+    env_fn = lambda: FixedSequenceEnv(n_actions=10, episode_len=5)
     learn = lambda e: get_learn_function(alg)(
         env=e,
         network=rnn,

baselines/common/tests/test_mnist.py

@@ -41,7 +41,7 @@ def test_mnist(alg):
 
     learn = get_learn_function(alg)
     learn_fn = lambda e: learn(env=e, **learn_kwargs)
-    env_fn = lambda: MnistEnv(seed=0, episode_len=100)
+    env_fn = lambda: MnistEnv(episode_len=100)
 
     simple_test(env_fn, learn_fn, 0.6)
 

baselines/common/tests/test_serialization.py

@@ -44,7 +44,12 @@ def test_serialization(learn_fn, network_fn):
             # github issue: https://github.com/openai/baselines/issues/660
             return
 
-    env = DummyVecEnv([lambda: MnistEnv(10, episode_len=100)])
+    def make_env():
+        env = MnistEnv(episode_len=100)
+        env.seed(10)
+        return env
+
+    env = DummyVecEnv([make_env])
     ob = env.reset().copy()
     learn = get_learn_function(learn_fn)
 

baselines/common/tests/util.py

@@ -1,17 +1,19 @@
 import tensorflow as tf
 import numpy as np
-from gym.spaces import np_random
 from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
 
 N_TRIALS = 10000
 N_EPISODES = 100
 
 def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=N_TRIALS):
+    def seeded_env_fn():
+        env = env_fn()
+        env.seed(0)
+        return env
+
     np.random.seed(0)
-    np_random.seed(0)
-
-    env = DummyVecEnv([env_fn])
 
+    env = DummyVecEnv([seeded_env_fn])
 
     with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default():
         tf.set_random_seed(0)