added a policy trained by DDPG

examples/pybullet/gym/agents/actor_net.py

@@ -0,0 +1,21 @@
+"""An actor network."""
+import tensorflow as tf
+import sonnet as snt
+
+class ActorNetwork(snt.AbstractModule):
+  """An actor network as a sonnet Module."""
+
+  def __init__(self, layer_sizes, action_size, name='target_actor'):
+    super(ActorNetwork, self).__init__(name=name)
+    self._layer_sizes = layer_sizes
+    self._action_size = action_size
+
+  def _build(self, inputs):
+    state = inputs
+    for output_size in self._layer_sizes:
+      state = snt.Linear(output_size)(state)
+      state = tf.nn.relu(state)
+
+    action = tf.tanh(
+        snt.Linear(self._action_size, name='action')(state))
+    return action

examples/pybullet/gym/agents/simpleAgent.py

@@ -0,0 +1,48 @@
+"""Loads a DDPG agent without too much external dependencies
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import collections
+import numpy as np
+import tensorflow as tf
+
+import sonnet as snt
+from agents import actor_net
+
+class SimpleAgent():
+  def __init__(
+      self,
+      session,
+      ckpt_path,
+      actor_layer_size,
+      observation_size=(31,),
+      action_size=8,
+  ):
+    self._ckpt_path = ckpt_path
+    self._actor_layer_size = actor_layer_size
+    self._observation_size = observation_size
+    self._action_size = action_size
+    self._session = session
+    self._build()
+
+  def _build(self):
+    self._agent_net = actor_net.ActorNetwork(self._actor_layer_size, self._action_size)
+    self._o_t = tf.placeholder(tf.float32, (31,))
+    with tf.name_scope('Act'):
+      batch_o_t = snt.nest.pack_iterable_as(
+          self._o_t,
+          snt.nest.map(
+              lambda x: tf.expand_dims(x, 0),
+              snt.nest.flatten_iterable(self._o_t)))
+      self._action = self._agent_net(batch_o_t)
+      saver = tf.train.Saver()
+      saver.restore(
+          sess=self._session,
+          save_path=self._ckpt_path)
+
+  def __call__(self, observation):
+    out_action = self._session.run(self._action, feed_dict={self._o_t: observation})
+    return out_action[0]