class DDPG:
"""docstring for DDPG"""
def __init__(self, environment):
self.name = 'DDPG' # name for uploading results
self.environment = environment
# Randomly initialize actor network and critic network
# with both their target networks
self.actor_network = ActorNetwork(state_size = environment.observation_space.shape[0],action_size = environment.action_space.shape[0])
self.critic_network = CriticNetwork(state_size = environment.observation_space.shape[0],action_size = environment.action_space.shape[0])
# initialize replay buffer
self.replay_buffer = deque()
# Initialize a random process the Ornstein-Uhlenbeck process for action exploration
self.exploration_noise = OUNoise(environment.action_space.shape[0])
# Initialize time step
self.time_step = 0
def set_init_observation(self,observation):
# receive initial observation state
self.state = observation
def train(self):
# Sample a random minibatch of N transitions from replay buffer
minibatch = random.sample(self.replay_buffer,BATCH_SIZE)
state_batch = [data[0] for data in minibatch]
action_batch = [data[1] for data in minibatch]
reward_batch = [data[2] for data in minibatch]
next_state_batch = [data[3] for data in minibatch]
action_batch = np.resize(action_batch,[BATCH_SIZE,1])
# Calculate y
y_batch = []
next_action_batch = self.actor_network.target_evaluate(next_state_batch)
q_value_batch = self.critic_network.target_evaluate(next_state_batch,next_action_batch)
for i in range(0,BATCH_SIZE):
done = minibatch[i][4]
if done:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
# Update critic by minimizing the loss L
self.critic_network.train(y_batch,state_batch,action_batch)
# Update the actor policy using the sampled gradient:
action_batch_for_gradients = self.actor_network.evaluate(state_batch)
q_gradient_batch = self.critic_network.gradients(state_batch,action_batch_for_gradients)/BATCH_SIZE
self.actor_network.train(q_gradient_batch,state_batch)
# Update the target networks
self.actor_network.update_target()
self.critic_network.update_target()
def get_action(self):
# Select action a_t according to the current policy and exploration noise
action = self.actor_network.get_action(self.state)
return np.clip(action+self.exploration_noise.noise(),self.environment.action_space.low,self.environment.action_space.high)
def set_feedback(self,observation,action,reward,done):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
next_state = observation
self.replay_buffer.append((self.state,action,reward,next_state,done))
# Update current state
self.state = next_state
# Update time step
self.time_step += 1
# Limit the replay buffer size
if len(self.replay_buffer) > REPLAY_BUFFER_SIZE:
self.replay_buffer.popleft()
# Store transitions to replay start size then start training
if self.time_step > REPLAY_START_SIZE:
self.train()
if self.time_step % 10000 == 0:
self.actor_network.save_network(self.time_step)
self.critic_network.save_network(self.time_step)
# Re-iniitialize the random process when an episode ends
if done:
self.exploration_noise.reset()
class DDPG:
"""docstring for DDPG"""
def __init__(self, environment):
self.name = 'DDPG' # name for uploading results
self.environment = environment
# Randomly initialize actor network and critic network
# with both their target networks
self.actor_network = ActorNetwork(
state_size=environment.observation_space.shape[0],
action_size=environment.action_space.shape[0])
self.critic_network = CriticNetwork(
state_size=environment.observation_space.shape[0],
action_size=environment.action_space.shape[0])
# initialize replay buffer
self.replay_buffer = deque()
# Initialize a random process the Ornstein-Uhlenbeck process for action exploration
self.exploration_noise = OUNoise(environment.action_space.shape[0])
# Initialize time step
self.time_step = 0
def set_init_observation(self, observation):
# receive initial observation state
self.state = observation
def train(self):
# Sample a random minibatch of N transitions from replay buffer
minibatch = random.sample(self.replay_buffer, BATCH_SIZE)
state_batch = [data[0] for data in minibatch]
action_batch = [data[1] for data in minibatch]
reward_batch = [data[2] for data in minibatch]
next_state_batch = [data[3] for data in minibatch]
action_batch = np.resize(action_batch, [BATCH_SIZE, 1])
# Calculate y
y_batch = []
next_action_batch = self.actor_network.target_evaluate(
next_state_batch)
q_value_batch = self.critic_network.target_evaluate(
next_state_batch, next_action_batch)
for i in range(0, BATCH_SIZE):
done = minibatch[i][4]
if done:
y_batch.append(reward_batch[i])
else:
y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
# Update critic by minimizing the loss L
self.critic_network.train(y_batch, state_batch, action_batch)
# Update the actor policy using the sampled gradient:
action_batch_for_gradients = self.actor_network.evaluate(state_batch)
q_gradient_batch = self.critic_network.gradients(
state_batch, action_batch_for_gradients) / BATCH_SIZE
self.actor_network.train(q_gradient_batch, state_batch)
# Update the target networks
self.actor_network.update_target()
self.critic_network.update_target()
def get_action(self):
# Select action a_t according to the current policy and exploration noise
action = self.actor_network.get_action(self.state)
return np.clip(action + self.exploration_noise.noise(),
self.environment.action_space.low,
self.environment.action_space.high)
def set_feedback(self, observation, action, reward, done):
# Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
next_state = observation
self.replay_buffer.append(
(self.state, action, reward, next_state, done))
# Update current state
self.state = next_state
# Update time step
self.time_step += 1
# Limit the replay buffer size
if len(self.replay_buffer) > REPLAY_BUFFER_SIZE:
self.replay_buffer.popleft()
# Store transitions to replay start size then start training
if self.time_step > REPLAY_START_SIZE:
self.train()
if self.time_step % 10000 == 0:
self.actor_network.save_network(self.time_step)
self.critic_network.save_network(self.time_step)
# Re-iniitialize the random process when an episode ends
if done:
self.exploration_noise.reset()
class ActorCriticNet:
def __init__(self,
input_dim,
action_dim,
critic_layers,
actor_layers,
actor_activation,
scope='ac_network'):
self.input_dim = input_dim
self.action_dim = action_dim
self.scope = scope
self.x = tf.placeholder(shape=(None, input_dim),
dtype=tf.float32,
name='x')
self.y = tf.placeholder(shape=(None, ), dtype=tf.float32, name='y')
with tf.variable_scope(scope):
self.actor_network = ActorNetwork(self.x,
action_dim,
hidden_layers=actor_layers,
activation=actor_activation)
self.critic_network = CriticNetwork(
self.x,
self.actor_network.get_output_layer(),
hidden_layers=critic_layers)
self.vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
tf.get_variable_scope().name)
self._build()
def _build(self):
value = self.critic_network.get_output_layer()
actor_loss = -tf.reduce_mean(value)
self.actor_vars = self.actor_network.get_params()
self.actor_grad = tf.gradients(actor_loss, self.actor_vars)
tf.summary.scalar("actor_loss", actor_loss, collections=['actor'])
self.actor_summary = tf.summary.merge_all('actor')
critic_loss = 0.5 * tf.reduce_mean(tf.square((value - self.y)))
self.critic_vars = self.critic_network.get_params()
self.critic_grad = tf.gradients(critic_loss, self.critic_vars)
tf.summary.scalar("critic_loss", critic_loss, collections=['critic'])
self.critic_summary = tf.summary.merge_all('critic')
def get_action(self, sess, state):
return self.actor_network.get_action(sess, state)
def get_value(self, sess, state):
return self.critic_network.get_value(sess, state)
def get_action_value(self, sess, state, action):
return self.critic_network.get_action_value(sess, state, action)
def get_actor_feed_dict(self, state):
return {self.x: state}
def get_critic_feed_dict(self, state, action, target):
return {
self.x: state,
self.y: target,
self.critic_network.input_action: action
}
def get_clone_op(self, network, tau=0.9):
update_ops = []
new_vars = {v.name.replace(network.scope, ''): v for v in network.vars}
for v in self.vars:
u = (1 - tau) * v + tau * new_vars[v.name.replace(self.scope, '')]
update_ops.append(tf.assign(v, u))
return update_ops
