removed ppo_lstm_mlp.

baselines/ppo2/layers.py

@@ -1,7 +1,7 @@
 import numpy as np
 import tensorflow as tf
 
-from baselines.a2c.utils import ortho_init, fc, lstm, lnlstm
+from baselines.a2c.utils import ortho_init, lstm, lnlstm
 from baselines.common.models import register, nature_cnn
 
 
@@ -53,26 +53,3 @@ def ppo_cnn_lstm(num_units=128, layer_norm=False, **conv_kwargs):
 @register("ppo_cnn_lnlstm")
 def ppo_cnn_lnlstm(num_units=128, **conv_kwargs):
     return ppo_cnn_lstm(num_units, layer_norm=True, **conv_kwargs)
-
-
-@register("ppo_lstm_mlp")
-def ppo_lstm_mlp(num_units=128, layer_norm=False):
-    def _network_fn(input, mask, state):
-        h = tf.layers.flatten(input)
-        mask = tf.to_float(mask)
-
-        if layer_norm:
-            h, next_state = lnlstm([h], [mask[:, None]], state, scope='lnlstm', nh=num_units)
-        else:
-            h, next_state = lstm([h], [mask[:, None]], state, scope='lstm', nh=num_units)
-        h = h[0]
-
-        num_layers = 2
-        num_hidden = 64
-        activation = tf.nn.relu
-        for i in range(num_layers):
-            h = fc(h, 'mlp_fc{}'.format(i), nh=num_hidden, init_scale=np.sqrt(2))
-            h = activation(h)
-        return h, next_state
-
-    return RNN(_network_fn, num_units * 2)