lstm network builders using tf lstm

baselines/common/tests/test_fixed_sequence.py

@@ -6,7 +6,8 @@ from baselines.run import get_learn_function
 
 common_kwargs = dict(
     seed=0,
-    total_timesteps=50000,
+    total_timesteps=20000,
+    nlstm=64
 )
     
 learn_kwargs = {
@@ -19,7 +20,7 @@ learn_kwargs = {
 
 
 alg_list = learn_kwargs.keys()
-rnn_list = ['lstm']
+rnn_list = ['lstm', 'tflstm', 'tflstm_static']
 
 @pytest.mark.slow
 @pytest.mark.parametrize("alg", alg_list)
@@ -41,11 +42,11 @@ def test_fixed_sequence(alg, rnn):
         **kwargs
     )
 
-    simple_test(env_fn, learn, 0.7)
+    simple_test(env_fn, learn, 0.3)
 
 
 if __name__ == '__main__':
-    test_fixed_sequence('ppo2', 'lstm')
+    test_fixed_sequence('ppo2', 'tflstm')
 
     
 

baselines/common/tests/util.py

@@ -2,6 +2,7 @@ import tensorflow as tf
 import numpy as np
 from gym.spaces import np_random
 from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
+from baselines.bench.monitor import Monitor
 
 N_TRIALS = 10000
 N_EPISODES = 100
@@ -10,7 +11,7 @@ def simple_test(env_fn, learn_fn, min_reward_fraction, n_trials=N_TRIALS):
     np.random.seed(0)
     np_random.seed(0)
 
-    env = DummyVecEnv([env_fn])
+    env = DummyVecEnv([lambda: Monitor(env_fn(), None, allow_early_resets=True)])
 
 
     with tf.Graph().as_default(), tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default():