auto target network. ppo_cartpole.py run ok. but results is different from previous version even with the same random seed, still needs debugging.
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haoshengzou
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@ -17,24 +17,9 @@ from rllab.envs.box2d.cartpole_env import CartpoleEnv
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from rllab.envs.normalized_env import normalize
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def policy_net(observation, action_dim, scope=None):
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"""
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Constructs the policy network. NOT NEEDED IN THE LIBRARY! this is pure tf
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# for tutorial purpose, placeholders are explicitly appended with '_ph' suffix
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:param observation: Placeholder for the observation. A tensor of shape (bs, x, y, channels)
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:param action_dim: int. The number of actions.
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:param scope: str. Specifying the scope of the variables.
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"""
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# with tf.variable_scope(scope):
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net = tf.layers.dense(observation, 32, activation=tf.nn.tanh)
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net = tf.layers.dense(net, 32, activation=tf.nn.tanh)
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act_mean = tf.layers.dense(net, action_dim, activation=None)
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return act_mean
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if __name__ == '__main__': # a clean version with only policy net, no value net
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if __name__ == '__main__':
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env = normalize(CartpoleEnv())
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observation_dim = env.observation_space.shape
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action_dim = env.action_space.flat_dim
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@ -47,44 +32,51 @@ if __name__ == '__main__': # a clean version with only policy net, no value net
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np.random.seed(seed)
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tf.set_random_seed(seed)
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# 1. build network with pure tf
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observation = tf.placeholder(tf.float32, shape=(None,) + observation_dim) # network input
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### 1. build network with pure tf
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observation_ph = tf.placeholder(tf.float32, shape=(None,) + observation_dim) # network input
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with tf.variable_scope('pi'):
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action_mean = policy_net(observation, action_dim, 'pi')
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action_logstd = tf.get_variable('action_logstd', shape=(action_dim,))
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train_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) # TODO: better management of TRAINABLE_VARIABLES
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with tf.variable_scope('pi_old'):
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action_mean_old = policy_net(observation, action_dim, 'pi_old')
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action_logstd_old = tf.get_variable('action_logstd_old', shape=(action_dim,))
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pi_old_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'pi_old')
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def my_policy():
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net = tf.layers.dense(observation_ph, 32, activation=tf.nn.tanh)
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net = tf.layers.dense(net, 32, activation=tf.nn.tanh)
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action_mean = tf.layers.dense(net, action_dim, activation=None)
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action_logstd = tf.get_variable('action_logstd', shape=(action_dim, ))
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# 2. build losses, optimizers
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pi = policy.Normal(action_mean, action_logstd, observation_placeholder=observation) # YongRen: policy.Gaussian (could reference the policy in TRPO paper, my code is adapted from zhusuan.distributions) policy.DQN etc.
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# for continuous action space, you may need to change an environment to run
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pi_old = policy.Normal(action_mean_old, action_logstd_old, observation_placeholder=observation)
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# value = tf.layers.dense(net, 1, activation=None)
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action = tf.placeholder(dtype=tf.float32, shape=(None, action_dim)) # batch of integer actions
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advantage = tf.placeholder(dtype=tf.float32, shape=(None,)) # advantage values used in the Gradients
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return action_mean, action_logstd, None # None value head
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ppo_loss_clip = losses.ppo_clip(action, advantage, clip_param, pi, pi_old) # TongzhengRen: losses.vpg ... management of placeholders and feed_dict
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# TODO: current implementation of passing function or overriding function has to return a value head
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# to allow network sharing between policy and value networks. This makes 'policy' and 'value_function'
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# imbalanced semantically (though they are naturally imbalanced since 'policy' is required to interact
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# with the environment and 'value_function' is not). I have an idea to solve this imbalance, which is
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# not based on passing function or overriding function.
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### 2. build policy, losses, optimizers
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pi = policy.Normal(my_policy, observation_placeholder=observation_ph, weight_update=0)
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# action = tf.placeholder(dtype=tf.float32, shape=(None, action_dim)) # batch of integer actions
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# advantage = tf.placeholder(dtype=tf.float32, shape=(None,)) # advantage values used in the Gradients
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ppo_loss_clip = losses.ppo_clip(pi, clip_param) # TongzhengRen: losses.vpg ... management of placeholders and feed_dict
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total_loss = ppo_loss_clip
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optimizer = tf.train.AdamOptimizer(1e-4)
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train_op = optimizer.minimize(total_loss, var_list=train_var_list)
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train_op = optimizer.minimize(total_loss, var_list=pi.trainable_variables)
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# 3. define data collection
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### 3. define data collection
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training_data = Batch(env, pi, advantage_estimation.full_return) # YouQiaoben: finish and polish Batch, advantage_estimation.gae_lambda as in PPO paper
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# ShihongSong: Replay(), see dqn_example.py
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# maybe a dict to manage the elements to be collected
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# 4. start training
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### 4. start training
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# init = tf.global_variables_initializer()
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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with tf.Session(config=config) as sess:
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sess.run(tf.global_variables_initializer())
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# sync pi and pi_old
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sess.run([tf.assign(theta_old, theta) for (theta_old, theta) in zip(pi_old_var_list, train_var_list)])
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pi.sync_weights() # TODO: automate this for policies with target network
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start_time = time.time()
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for i in range(100): # until some stopping criterion met...
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@ -97,11 +89,10 @@ if __name__ == '__main__': # a clean version with only policy net, no value net
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# update network
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for _ in range(num_batches):
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data = training_data.next_batch(batch_size) # YouQiaoben, ShihongSong
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# TODO: auto managing of the placeholders? or add this to params of data.Batch
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sess.run(train_op, feed_dict={observation: data['observations'], action: data['actions'], advantage: data['returns']})
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feed_dict = training_data.next_batch(batch_size) # YouQiaoben, ShihongSong
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sess.run(train_op, feed_dict=feed_dict)
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# assigning pi to pi_old
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sess.run([tf.assign(theta_old, theta) for (theta_old, theta) in zip(pi_old_var_list, train_var_list)])
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pi.update_weights()
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print('Elapsed time: {:.1f} min'.format((time.time() - start_time) / 60))
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@ -1,22 +1,26 @@
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import tensorflow as tf
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def ppo_clip(sampled_action, advantage, clip_param, pi, pi_old):
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def ppo_clip(policy, clip_param):
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"""
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the clip loss in ppo paper
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:param sampled_action: placeholder of sampled actions during interaction with the environment
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:param advantage: placeholder of estimated advantage values.
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:param clip param: float or Tensor of type float.
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:param pi: current `policy` to be optimized
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:param policy: current `policy` to be optimized
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:param pi_old: old `policy` for computing the ppo loss as in Eqn. (7) in the paper
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"""
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action_ph = tf.placeholder(policy.act_dtype, shape=(None, policy.action_dim), name='ppo_clip_loss/action_placeholder')
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advantage_ph = tf.placeholder(tf.float32, shape=(None,), name='ppo_clip_loss/advantage_placeholder')
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policy.managed_placeholders['action'] = action_ph
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policy.managed_placeholders['processed_reward'] = advantage_ph
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log_pi_act = pi.log_prob(sampled_action)
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log_pi_old_act = pi_old.log_prob(sampled_action)
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log_pi_act = policy.log_prob(action_ph)
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log_pi_old_act = policy.log_prob_old(action_ph)
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ratio = tf.exp(log_pi_act - log_pi_old_act)
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clipped_ratio = tf.clip_by_value(ratio, 1. - clip_param, 1. + clip_param)
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ppo_clip_loss = -tf.reduce_mean(tf.minimum(ratio * advantage, clipped_ratio * advantage))
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ppo_clip_loss = -tf.reduce_mean(tf.minimum(ratio * advantage_ph, clipped_ratio * advantage_ph))
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return ppo_clip_loss
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@ -16,44 +16,11 @@ class PolicyBase(object):
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"""
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base class for policy. only provides `act` method with exploration
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"""
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def __init__(self, observation_placeholder):
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self._observation_placeholder = observation_placeholder
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def act(self, observation, exploration):
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def act(self, observation):
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raise NotImplementedError()
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class QValuePolicy(object):
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"""
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The policy as in DQN
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"""
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def __init__(self, observation_placeholder):
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self._observation_placeholder = observation_placeholder
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def act(self, observation, exploration=None): # first implement no exploration
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"""
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return the action (int) to be executed.
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no exploration when exploration=None.
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"""
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self._act(observation, exploration)
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def _act(self, observation, exploration=None):
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raise NotImplementedError()
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def values(self, observation):
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"""
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returns the Q(s, a) values (float) for all actions a at observation s
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"""
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pass
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def values_tensor(self):
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"""
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returns the tensor of the values for all actions a at observation s
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"""
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pass
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class StochasticPolicy(object):
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class StochasticPolicy(PolicyBase):
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"""
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The :class:`StochasticPolicy` class is the base class for various probabilistic
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distributions which support batch inputs, generating batches of samples and
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@ -170,7 +137,7 @@ class StochasticPolicy(object):
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return self._group_ndims
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# @add_name_scope
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def act(self, observation):
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def act(self, observation, my_feed_dict={}):
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"""
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sample(n_samples=None)
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@ -184,9 +151,9 @@ class StochasticPolicy(object):
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samples to draw from the distribution.
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:return: A Tensor of samples.
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"""
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return self._act(observation)
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return self._act(observation, my_feed_dict)
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def _act(self, observation):
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def _act(self, observation, my_feed_dict):
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"""
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Private method for subclasses to rewrite the :meth:`sample` method.
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"""
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@ -94,24 +94,64 @@ class Normal(StochasticPolicy):
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:param observation_placeholder
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"""
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def __init__(self,
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mean = 0.,
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logstd = 1.,
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group_ndims = 1,
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observation_placeholder = None,
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policy_callable,
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observation_placeholder,
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weight_update=1,
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group_ndims=1,
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**kwargs):
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self.managed_placeholders = {'observation': observation_placeholder}
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self.weight_update = weight_update
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self.interaction_count = -1 # defaults to -1. only useful if weight_update > 1.
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with tf.variable_scope('network'):
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mean, logstd, value_head = policy_callable()
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self._mean = tf.convert_to_tensor(mean, dtype = tf.float32)
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self._logstd = tf.convert_to_tensor(logstd, dtype = tf.float32)
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self._std = tf.exp(self._logstd)
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shape = tf.broadcast_dynamic_shape(tf.shape(self._mean), tf.shape(self._std))
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self._action = tf.random_normal(tf.concat([[1], shape], 0), dtype = tf.float32) * self._std + self._mean
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# TODO: self._action should be exactly the action tensor to run, without [0, 0] in self._act
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if value_head is not None:
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pass
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self.trainable_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='network')
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if self.weight_update == 1:
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self.weight_update_ops = None
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self.sync_weights_ops = None
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else: # then we need to build another tf graph as target network
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with tf.variable_scope('net_old'):
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mean, logstd, value_head = policy_callable()
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self._mean_old = tf.convert_to_tensor(mean, dtype=tf.float32)
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self._logstd_old = tf.convert_to_tensor(logstd, dtype=tf.float32)
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if value_head is not None: # useful in DDPG
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pass
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network_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='network')
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self.network_old_weights = network_old_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='net_old')
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assert len(network_weights) == len(network_old_weights)
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self.sync_weights_ops = [tf.assign(variable_old, variable)
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for (variable_old, variable) in zip(network_old_weights, network_weights)]
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if weight_update == 0:
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self.weight_update_ops = self.sync_weights_ops
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elif 0 < weight_update < 1:
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pass
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else:
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self.interaction_count = 0
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import math
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self.weight_update = math.ceil(weight_update)
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super(Normal, self).__init__(
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act_dtype = tf.float32,
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param_dtype = tf.float32,
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is_continuous = True,
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observation_placeholder = observation_placeholder,
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act_dtype=tf.float32,
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param_dtype=tf.float32,
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is_continuous=True,
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observation_placeholder=observation_placeholder,
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group_ndims = group_ndims,
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**kwargs)
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@ -127,13 +167,22 @@ class Normal(StochasticPolicy):
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def logstd(self):
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return self._logstd
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def _act(self, observation):
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@property
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def action(self):
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return self._action
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@property
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def action_dim(self):
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return self.mean.shape.as_list()[-1]
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def _act(self, observation, my_feed_dict):
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# TODO: getting session like this maybe ugly. also maybe huge problem when parallel
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sess = tf.get_default_session()
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# observation[None] adds one dimension at the beginning
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sampled_action = sess.run(self._action,
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feed_dict={self._observation_placeholder: observation[None]})
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feed_dict = {self._observation_placeholder: observation[None]}
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feed_dict.update(my_feed_dict)
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sampled_action = sess.run(self._action, feed_dict=feed_dict)
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sampled_action = sampled_action[0, 0]
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return sampled_action
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@ -146,3 +195,35 @@ class Normal(StochasticPolicy):
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def _prob(self, sampled_action):
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return tf.exp(self._log_prob(sampled_action))
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def log_prob_old(self, sampled_action):
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"""
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return the log_prob of the old policy when constructing tf graphs. Raises error when there's no old policy.
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:param sampled_action: the placeholder for sampled actions during interaction with the environment.
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:return: tensor of the log_prob of the old policy
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"""
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if self.weight_update == 1:
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raise AttributeError('Policy has no policy_old since it\'s initialized with weight_update=1!')
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mean, logstd = self._mean_old, self._logstd_old
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c = -0.5 * np.log(2 * np.pi)
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precision = tf.exp(-2 * logstd)
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return c - logstd - 0.5 * precision * tf.square(sampled_action - mean)
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def update_weights(self):
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"""
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updates the weights of policy_old.
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:return:
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"""
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if self.weight_update_ops is not None:
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sess = tf.get_default_session()
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sess.run(self.weight_update_ops)
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def sync_weights(self):
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"""
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sync the weights of network_old. Direct copy the weights of network.
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:return:
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"""
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if self.sync_weights_ops is not None:
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sess = tf.get_default_session()
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sess.run(self.sync_weights_ops)
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@ -135,7 +135,12 @@ class Batch(object):
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advantage_std = np.std(current_batch['returns'])
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current_batch['returns'] = (current_batch['returns'] - advantage_mean) / advantage_std
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return current_batch
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feed_dict = {}
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feed_dict[self._pi.managed_placeholders['observation']] = current_batch['observations']
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feed_dict[self._pi.managed_placeholders['action']] = current_batch['actions']
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feed_dict[self._pi.managed_placeholders['processed_reward']] = current_batch['returns']
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return feed_dict
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# TODO: this will definitely be refactored with a proper logger
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def statistics(self):
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