From 2a4ba2b0a5b607d608e3f0a22779cf8b51b46cd9 Mon Sep 17 00:00:00 2001
From: gyunt <truth0233@gmail.com>
Date: Wed, 27 Mar 2019 07:54:15 +0900
Subject: [PATCH] add RNN layers.

---
 baselines/ppo2/layers.py | 105 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 104 insertions(+), 1 deletion(-)

diff --git a/baselines/ppo2/layers.py b/baselines/ppo2/layers.py
index 90d42ec..04c5e0d 100644
--- a/baselines/ppo2/layers.py
+++ b/baselines/ppo2/layers.py
@@ -1,7 +1,7 @@
 import numpy as np
 import tensorflow as tf
 
-from baselines.a2c.utils import ortho_init
+from baselines.a2c.utils import ortho_init, fc
 from baselines.common.models import register
 
 
@@ -88,6 +88,93 @@ def ppo_cnn_lnlstm(nlstm=128, **conv_kwargs):
     return ppo_cnn_lstm(nlstm, layer_norm=True, **conv_kwargs)
 
 
+@register("ppo_gru")
+def ppo_gru(nlstm=128):
+    def network_fn(input, mask):
+        memory_size = nlstm
+        nbatch = input.shape[0]
+        mask.get_shape().assert_is_compatible_with([nbatch])
+        state = tf.Variable(np.zeros([nbatch, memory_size]),
+                            name='gru_state',
+                            trainable=False,
+                            dtype=tf.float32,
+                            collections=[tf.GraphKeys.LOCAL_VARIABLES])
+
+        def _network_fn(input, mask, state):
+            input = tf.layers.flatten(input)
+            mask = tf.to_float(mask)
+
+            h, next_state = gru(input, mask, state, nh=nlstm)
+            return h, next_state
+
+        return state, _network_fn
+
+    return network_fn
+
+
+@register("ppo_lstm_mlp")
+def ppo_lstm(nlstm=128, layer_norm=False):
+    def network_fn(input, mask):
+        memory_size = nlstm * 2
+        nbatch = input.shape[0]
+        mask.get_shape().assert_is_compatible_with([nbatch])
+        state = tf.Variable(np.zeros([nbatch, memory_size]),
+                            name='lstm_state',
+                            trainable=False,
+                            dtype=tf.float32,
+                            collections=[tf.GraphKeys.LOCAL_VARIABLES])
+
+        def _network_fn(input, mask, state):
+            input = tf.layers.flatten(input)
+            mask = tf.to_float(mask)
+
+            h, next_state = lstm(input, mask, state, scope='lstm', nh=nlstm)
+
+            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 state, _network_fn
+
+    return network_fn
+
+
+@register("ppo_gru_mlp")
+def ppo_gru_mlp(nlstm=128):
+    def network_fn(input, mask):
+        memory_size = nlstm
+        nbatch = input.shape[0]
+        mask.get_shape().assert_is_compatible_with([nbatch])
+        state = tf.Variable(np.zeros([nbatch, memory_size]),
+                            name='gru_state',
+                            trainable=False,
+                            dtype=tf.float32,
+                            collections=[tf.GraphKeys.LOCAL_VARIABLES])
+
+        def _network_fn(input, mask, state):
+            input = tf.layers.flatten(input)
+            mask = tf.to_float(mask)
+
+            h, next_state = gru(input, mask, state, nh=nlstm)
+
+            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 state, _network_fn
+
+    return network_fn
+
+
 def lstm(x, m, s, scope, nh, init_scale=1.0):
     x = tf.layers.flatten(x)
     nin = x.get_shape()[1]
@@ -155,3 +242,19 @@ def lnlstm(x, m, s, scope, nh, init_scale=1.0):
     s = tf.concat(axis=1, values=[c, h])
 
     return h, s
+
+
+def gru(x, mask, state, nh, init_scale=-1.0):
+    """Gated recurrent unit (GRU) with nunits cells."""
+    h = state
+    mask = tf.tile(tf.expand_dims(mask, axis=-1), multiples=[1, nh])
+
+    h *= (1.0 - mask)
+    hx = tf.concat([h, x], axis=1)
+    mr = tf.sigmoid(fc(hx, nh=nh * 2, scope='gru_mr', init_bias=init_scale))
+    # r: read strength. m: 'member strength
+    m, r = tf.split(mr, 2, axis=1)
+    rh_x = tf.concat([r * h, x], axis=1)
+    htil = tf.tanh(fc(rh_x, nh=nh, scope='gru_htil'))
+    h = m * h + (1.0 - m) * htil
+    return h, h