lstm network builders using tf lstm

baselines/common/models.py

@@ -92,6 +92,48 @@ def lstm(nlstm=128, layer_norm=False):
 
     return network_fn
 
+def tflstm_static(nlstm=128, layer_norm=False):
+    def network_fn(X, nenv=1):
+        nbatch = X.shape[0] 
+        nsteps = nbatch // nenv
+         
+        h = tf.layers.flatten(X)
+        rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(nlstm, state_is_tuple=False, forget_bias=0.0)
+
+        S = tf.placeholder(tf.float32, rnn_cell.zero_state(nenv, dtype=tf.float32).shape) #states
+        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
+
+        xs = batch_to_seq(h, nenv, nsteps)
+
+        h5, snew = tf.nn.static_rnn(rnn_cell, xs, initial_state=S)
+           
+        h = seq_to_batch(h5)
+
+        initial_state = np.zeros(S.shape.as_list(), dtype=float)
+
+        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}
+
+    return network_fn
+
+def tflstm(nlstm=128):
+    def network_fn(X, nenv=1):
+        nbatch = X.shape[0] 
+        nsteps = nbatch // nenv
+         
+        h = tf.layers.flatten(X)
+        rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(nlstm, state_is_tuple=False, forget_bias=0.0)
+
+        S = tf.placeholder(tf.float32, rnn_cell.zero_state(nenv, dtype=tf.float32).shape) #states
+        M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
+        initial_state = np.zeros(S.shape)
+
+        h = tf.reshape(h, (-1, nsteps, h.shape[-1]))
+        h, snew = tf.nn.dynamic_rnn(rnn_cell, h, initial_state=S)     
+
+        h = tf.reshape(h, (-1, h.shape[-1]))
+        return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}
+
+    return network_fn
 
 def cnn_lstm(nlstm=128, layer_norm=False, **conv_kwargs):
     def network_fn(X, nenv=1):
@@ -169,6 +211,10 @@ def get_network_builder(name):
         return mlp
     elif name == 'lstm':
         return lstm
+    elif name == 'tflstm_static':
+        return tflstm_static
+    elif name == 'tflstm':
+        return tflstm
     elif name == 'cnn_lstm':
         return cnn_lstm
     elif name == 'cnn_lnlstm':

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():