finish ddpg example. all examples under examples/ (except those containing 'contrib' and 'fail') can run! advantage estimation module is not complete yet.

examples/actor_critic_cartpole.py

@@ -49,10 +49,10 @@ if __name__ == '__main__':
 
     ### 2. build policy, critic, loss, optimizer
     actor = policy.OnehotCategorical(my_network, observation_placeholder=observation_ph, weight_update=1)
-    critic = value_function.StateValue(my_network, observation_placeholder=observation_ph)
+    critic = value_function.StateValue(my_network, observation_placeholder=observation_ph)  # no target network
 
     actor_loss = losses.REINFORCE(actor)
-    critic_loss = losses.state_value_mse(critic)
+    critic_loss = losses.value_mse(critic)
     total_loss = actor_loss + critic_loss
 
     optimizer = tf.train.AdamOptimizer(1e-4)

examples/actor_critic_fail_cartpole.py

@@ -57,7 +57,7 @@ if __name__ == '__main__':
 
 
     actor_loss = losses.vanilla_policy_gradient(actor)
-    critic_loss = losses.state_value_mse(critic)
+    critic_loss = losses.value_mse(critic)
     total_loss = actor_loss + critic_loss
 
     optimizer = tf.train.AdamOptimizer(1e-4)

examples/actor_critic_separate_cartpole.py

@@ -51,7 +51,7 @@ if __name__ == '__main__':
     critic = value_function.StateValue(my_network, observation_placeholder=observation_ph)
 
     actor_loss = losses.REINFORCE(actor)
-    critic_loss = losses.state_value_mse(critic)
+    critic_loss = losses.value_mse(critic)
 
     actor_optimizer = tf.train.AdamOptimizer(1e-4)
     actor_train_op = actor_optimizer.minimize(actor_loss, var_list=actor.trainable_variables)

examples/ddpg_example.py

@@ -0,0 +1,89 @@
+#!/usr/bin/env python
+from __future__ import absolute_import
+
+import tensorflow as tf
+import gym
+import numpy as np
+import time
+
+# our lib imports here! It's ok to append path in examples
+import sys
+sys.path.append('..')
+from tianshou.core import losses
+from tianshou.data.batch import Batch
+import tianshou.data.advantage_estimation as advantage_estimation
+import tianshou.core.policy as policy
+import tianshou.core.value_function.action_value as value_function
+import tianshou.core.opt as opt
+
+
+if __name__ == '__main__':
+    env = gym.make('Pendulum-v0')
+    observation_dim = env.observation_space.shape
+    action_dim = env.action_space.shape
+
+    clip_param = 0.2
+    num_batches = 10
+    batch_size = 512
+
+    seed = 0
+    np.random.seed(seed)
+    tf.set_random_seed(seed)
+
+    ### 1. build network with pure tf
+    observation_ph = tf.placeholder(tf.float32, shape=(None,) + observation_dim)
+    action_ph = tf.placeholder(tf.float32, shape=(None,) + action_dim)
+
+    def my_network():
+        net = tf.layers.dense(observation_ph, 32, activation=tf.nn.relu)
+        net = tf.layers.dense(net, 32, activation=tf.nn.relu)
+        action = tf.layers.dense(net, action_dim[0], activation=None)
+
+        action_value_input = tf.concat([observation_ph, action_ph], axis=1)
+        net = tf.layers.dense(action_value_input, 32, activation=tf.nn.relu)
+        net = tf.layers.dense(net, 32, activation=tf.nn.relu)
+        action_value = tf.layers.dense(net, 1, activation=None)
+
+        return action, action_value
+
+    ### 2. build policy, loss, optimizer
+    actor = policy.Deterministic(my_network, observation_placeholder=observation_ph, weight_update=1e-3)
+    critic = value_function.ActionValue(my_network, observation_placeholder=observation_ph,
+                                        action_placeholder=action_ph, weight_update=1e-3)
+
+    critic_loss = losses.value_mse(critic)
+    critic_optimizer = tf.train.AdamOptimizer(1e-3)
+    critic_train_op = critic_optimizer.minimize(critic_loss, var_list=critic.trainable_variables)
+
+    dpg_grads = opt.DPG(actor, critic)  # not sure if it's correct
+    actor_optimizer = tf.train.AdamOptimizer(1e-4)
+    actor_train_op = actor_optimizer.apply_gradients(dpg_grads)
+
+    ### 3. define data collection
+    data_collector = Batch(env, actor, [advantage_estimation.ddpg_return(actor, critic)], [actor, critic])
+
+    ### 4. start training
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    with tf.Session(config=config) as sess:
+        sess.run(tf.global_variables_initializer())
+
+        # assign actor to pi_old
+        actor.sync_weights()  # TODO: automate this for policies with target network
+        critic.sync_weights()
+
+        start_time = time.time()
+        for i in range(100):
+            # collect data
+            data_collector.collect(num_episodes=50)
+
+            # print current return
+            print('Epoch {}:'.format(i))
+            data_collector.statistics()
+
+            # update network
+            for _ in range(num_batches):
+                feed_dict = data_collector.next_batch(batch_size)
+                sess.run([actor_train_op, critic_train_op], feed_dict=feed_dict)
+
+            print('Elapsed time: {:.1f} min'.format((time.time() - start_time) / 60))

examples/dqn_example.py

@@ -16,6 +16,9 @@ import tianshou.core.policy.dqn as policy  # TODO: fix imports as zhusuan so tha
 import tianshou.core.value_function.action_value as value_function
 
 
+# TODO: why this solves cartpole even without training?
+
+
 if __name__ == '__main__':
     env = gym.make('CartPole-v0')
     observation_dim = env.observation_space.shape

tianshou/core/losses.py

@@ -45,13 +45,13 @@ def REINFORCE(policy):
     return REINFORCE_loss
 
 
-def state_value_mse(state_value_function):
+def value_mse(state_value_function):
     """
     L2 loss of state value
     :param state_value_function: instance of StateValue
     :return: tensor of the mse loss
     """
-    target_value_ph = tf.placeholder(tf.float32, shape=(None,), name='state_value_mse/state_value_placeholder')
+    target_value_ph = tf.placeholder(tf.float32, shape=(None,), name='value_mse/return_placeholder')
     state_value_function.managed_placeholders['return'] = target_value_ph
 
     state_value = state_value_function.value_tensor

tianshou/core/opt.py

@@ -0,0 +1,21 @@
+import tensorflow as tf
+
+
+def DPG(policy, action_value):
+    """
+    construct the gradient tensor of deterministic policy gradient
+    :param policy:
+    :param action_value:
+    :return: list of (gradient, variable) pairs
+    """
+    trainable_variables = policy.trainable_variables
+    critic_action_input = action_value._action_placeholder
+    critic_value_loss = -tf.reduce_mean(action_value.value_tensor)
+    policy_action_output = policy.action
+
+    grad_ys = tf.gradients(critic_value_loss, critic_action_input)
+    grad_policy_vars = tf.gradients(policy_action_output, trainable_variables, grad_ys=grad_ys)
+
+    grads_and_vars = zip(grad_policy_vars, trainable_variables)
+
+    return grads_and_vars

tianshou/core/policy/__init__.py

@@ -0,0 +1,3 @@
+from .deterministic import *
+from .dqn import *
+from .stochastic import *

tianshou/core/policy/deterministic.py

@@ -0,0 +1,111 @@
+import tensorflow as tf
+from .base import PolicyBase
+
+class Deterministic(PolicyBase):
+    """
+    deterministic policy as used in deterministic policy gradient methods
+    """
+    def __init__(self, policy_callable, observation_placeholder, weight_update=1):
+        self._observation_placeholder = observation_placeholder
+        self.managed_placeholders = {'observation': observation_placeholder}
+        self.weight_update = weight_update
+        self.interaction_count = -1  # defaults to -1. only useful if weight_update > 1.
+
+        # build network, action and value
+        with tf.variable_scope('network', reuse=tf.AUTO_REUSE):
+            action, _ = policy_callable()
+            self.action = action
+            # TODO: self._action should be exactly the action tensor to run that directly gives action_dim
+
+        self.trainable_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='network')
+
+        # deal with target network
+        if self.weight_update == 1:
+            self.weight_update_ops = None
+            self.sync_weights_ops = None
+        else:  # then we need to build another tf graph as target network
+            with tf.variable_scope('net_old', reuse=tf.AUTO_REUSE):
+                action, _ = policy_callable()
+                self.action_old = action
+
+            network_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='network')
+            network_old_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='net_old')
+            # TODO: use a scope that the user will almost surely not use. so get_collection will return
+            # the correct weights and old_weights, since it filters by regular expression
+            # or we write a util to parse the variable names and use only the topmost scope
+
+            assert len(network_weights) == len(network_old_weights)
+            self.sync_weights_ops = [tf.assign(variable_old, variable)
+                                     for (variable_old, variable) in zip(network_old_weights, network_weights)]
+
+            if weight_update == 0:
+                self.weight_update_ops = self.sync_weights_ops
+            elif 0 < weight_update < 1:  # as in DDPG
+                self.weight_update_ops = [tf.assign(variable_old,
+                                                    weight_update * variable + (1 - weight_update) * variable_old)
+                                          for (variable_old, variable) in zip(network_old_weights, network_weights)]
+            else:
+                self.interaction_count = 0  # as in DQN
+                import math
+                self.weight_update = math.ceil(weight_update)
+
+    @property
+    def action_shape(self):
+        return self.action.shape.as_list()[1:]
+
+    def act(self, observation, my_feed_dict={}):
+        # TODO: this may be ugly. also maybe huge problem when parallel
+        sess = tf.get_default_session()
+        # observation[None] adds one dimension at the beginning
+
+        feed_dict = {self._observation_placeholder: observation[None]}
+        feed_dict.update(my_feed_dict)
+        sampled_action = sess.run(self.action, feed_dict=feed_dict)
+
+        sampled_action = sampled_action[0]
+
+        return sampled_action
+
+    def update_weights(self):
+        """
+        updates the weights of policy_old.
+        :return:
+        """
+        if self.weight_update_ops is not None:
+            sess = tf.get_default_session()
+            sess.run(self.weight_update_ops)
+
+    def sync_weights(self):
+        """
+        sync the weights of network_old. Direct copy the weights of network.
+        :return:
+        """
+        if self.sync_weights_ops is not None:
+            sess = tf.get_default_session()
+            sess.run(self.sync_weights_ops)
+
+    def eval_action(self, observation):
+        """
+        evaluate action in minibatch
+        :param observation:
+        :return: 2-D numpy array
+        """
+        sess = tf.get_default_session()
+
+        feed_dict = {self._observation_placeholder: observation}
+        action = sess.run(self.action, feed_dict=feed_dict)
+
+        return action
+
+    def eval_action_old(self, observation):
+        """
+        evaluate action in minibatch
+        :param observation:
+        :return: 2-D numpy array
+        """
+        sess = tf.get_default_session()
+
+        feed_dict = {self._observation_placeholder: observation}
+        action = sess.run(self.action_old, feed_dict=feed_dict)
+
+        return action

tianshou/core/value_function/action_value.py

@@ -8,12 +8,47 @@ class ActionValue(ValueFunctionBase):
     """
     class of action values Q(s, a).
     """
-    def __init__(self, value_tensor, observation_placeholder, action_placeholder):
+    def __init__(self, network_callable, observation_placeholder, action_placeholder, weight_update=1):
+        self._observation_placeholder = observation_placeholder
         self._action_placeholder = action_placeholder
-        super(ActionValue, self).__init__(
-            value_tensor=value_tensor,
-            observation_placeholder=observation_placeholder
-        )
+        self.managed_placeholders = {'observation': observation_placeholder, 'action': action_placeholder}
+        self.weight_update = weight_update
+        self.interaction_count = -1  # defaults to -1. only useful if weight_update > 1.
+
+        with tf.variable_scope('network', reuse=tf.AUTO_REUSE):
+            value_tensor = network_callable()[-1]
+
+        self.trainable_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='network')
+
+        super(ActionValue, self).__init__(value_tensor, observation_placeholder=observation_placeholder)
+
+        # deal with target network
+        if self.weight_update == 1:
+            self.weight_update_ops = None
+            self.sync_weights_ops = None
+        else:  # then we need to build another tf graph as target network
+            with tf.variable_scope('net_old', reuse=tf.AUTO_REUSE):
+                value_tensor = network_callable()[-1]
+                self.value_tensor_old = tf.squeeze(value_tensor)
+
+        network_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='network')
+        network_old_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='net_old')
+
+        assert len(network_weights) == len(network_old_weights)
+        self.sync_weights_ops = [tf.assign(variable_old, variable)
+                                 for (variable_old, variable) in zip(network_old_weights, network_weights)]
+
+        if weight_update == 0:
+            self.weight_update_ops = self.sync_weights_ops
+        elif 0 < weight_update < 1:  # useful in DDPG
+            self.weight_update_ops = [tf.assign(variable_old,
+                                                weight_update * variable + (1 - weight_update) * variable_old)
+                                      for (variable_old, variable) in zip(network_old_weights, network_weights)]
+        else:
+            self.interaction_count = 0
+            import math
+            self.weight_update = math.ceil(weight_update)
+            self.weight_update_ops = self.sync_weights_ops
 
     def eval_value(self, observation, action):
         """
@@ -27,6 +62,35 @@ class ActionValue(ValueFunctionBase):
         return sess.run(self.value_tensor, feed_dict=
         {self._observation_placeholder: observation, self._action_placeholder: action})
 
+    def eval_value_old(self, observation, action):
+        """
+        eval value using target network
+        :param observation: numpy array of obs
+        :param action: numpy array of action
+        :return: numpy array of action value
+        """
+        sess = tf.get_default_session()
+        feed_dict = {self._observation_placeholder: observation, self._action_placeholder: action}
+        return sess.run(self.value_tensor_old, feed_dict=feed_dict)
+
+    def sync_weights(self):
+        """
+        sync the weights of network_old. Direct copy the weights of network.
+        :return:
+        """
+        if self.sync_weights_ops is not None:
+            sess = tf.get_default_session()
+            sess.run(self.sync_weights_ops)
+
+    def update_weights(self):
+        """
+        updates the weights of policy_old.
+        :return:
+        """
+        if self.weight_update_ops is not None:
+            sess = tf.get_default_session()
+            sess.run(self.weight_update_ops)
+
 
 class DQN(ValueFunctionBase):
     """

tianshou/data/advantage_estimation.py

@@ -76,6 +76,31 @@ class nstep_return:
         return {'return': return_}
 
 
+class ddpg_return:
+    """
+    compute the return as in DDPG. this seems to have to be special
+    """
+    def __init__(self, actor, critic, use_target_network=True):
+        self.actor = actor
+        self.critic = critic
+        self.use_target_network = use_target_network
+
+    def __call__(self, raw_data):
+        observation = raw_data['observation']
+        reward = raw_data['reward']
+
+        if self.use_target_network:
+            action_target = self.actor.eval_action_old(observation)
+            value_target = self.critic.eval_value_old(observation, action_target)
+        else:
+            action_target = self.actor.eval_action(observation)
+            value_target = self.critic.eval_value(observation, action_target)
+
+        return_ = reward + value_target
+
+        return {'return': return_}
+
+
 class nstep_q_return:
     """
     compute the n-step return for Q-learning targets