import copy
from logging import getLogger
import chainer
from chainer import cuda
import chainer.functions as F
from chainerrl.agent import Agent
from chainerrl.agent import AttributeSavingMixin
from chainerrl.agents.ddpg import disable_train
from chainerrl.misc.batch_states import batch_states
from chainerrl.misc.copy_param import synchronize_parameters
from chainerrl.recurrent import Recurrent
from chainerrl.replay_buffer import batch_experiences
from chainerrl.replay_buffer import ReplayUpdater
[docs]class PGT(AttributeSavingMixin, Agent):
"""Policy Gradient Theorem with an approximate policy and a Q-function.
This agent is almost the same with DDPG except that it uses the likelihood
ratio gradient estimation instead of value gradients.
Args:
model (chainer.Chain): Chain that contains both a policy and a
Q-function
actor_optimizer (Optimizer): Optimizer setup with the policy
critic_optimizer (Optimizer): Optimizer setup with the Q-function
replay_buffer (ReplayBuffer): Replay buffer
gamma (float): Discount factor
explorer (Explorer): Explorer that specifies an exploration strategy.
gpu (int): GPU device id. -1 for CPU.
replay_start_size (int): if the replay buffer's size is less than
replay_start_size, skip update
minibatch_size (int): Minibatch size
update_interval (int): Model update interval in step
target_update_interval (int): Target model update interval in step
phi (callable): Feature extractor applied to observations
target_update_method (str): 'hard' or 'soft'.
soft_update_tau (float): Tau of soft target update.
n_times_update (int): Number of repetition of update
average_q_decay (float): Decay rate of average Q, only used for
recording statistics
average_loss_decay (float): Decay rate of average loss, only used for
recording statistics
batch_accumulator (str): 'mean' or 'sum'
logger (Logger): Logger used
beta (float): Coefficient for entropy regularization
act_deterministically (bool): Act deterministically by selecting most
probable actions in test time
batch_states (callable): method which makes a batch of observations.
default is `chainerrl.misc.batch_states.batch_states`
"""
saved_attributes = ('model',
'target_model',
'actor_optimizer',
'critic_optimizer')
def __init__(self, model, actor_optimizer, critic_optimizer, replay_buffer,
gamma, explorer, beta=1e-2, act_deterministically=False,
gpu=-1, replay_start_size=50000,
minibatch_size=32, update_interval=1,
target_update_interval=10000,
phi=lambda x: x,
target_update_method='hard',
soft_update_tau=1e-2,
n_times_update=1, average_q_decay=0.999,
average_loss_decay=0.99,
logger=getLogger(__name__),
batch_states=batch_states):
self.model = model
if gpu is not None and gpu >= 0:
cuda.get_device_from_id(gpu).use()
self.model.to_gpu(device=gpu)
self.xp = self.model.xp
self.replay_buffer = replay_buffer
self.gamma = gamma
self.explorer = explorer
self.gpu = gpu
self.target_update_interval = target_update_interval
self.phi = phi
self.target_update_method = target_update_method
self.soft_update_tau = soft_update_tau
self.logger = logger
self.average_q_decay = average_q_decay
self.average_loss_decay = average_loss_decay
self.actor_optimizer = actor_optimizer
self.critic_optimizer = critic_optimizer
self.beta = beta
self.act_deterministically = act_deterministically
self.replay_updater = ReplayUpdater(
replay_buffer=replay_buffer,
update_func=self.update,
batchsize=minibatch_size,
episodic_update=False,
n_times_update=n_times_update,
replay_start_size=replay_start_size,
update_interval=update_interval,
)
self.batch_states = batch_states
self.t = 0
self.last_state = None
self.last_action = None
self.target_model = copy.deepcopy(self.model)
disable_train(self.target_model['q_function'])
disable_train(self.target_model['policy'])
self.average_q = 0
self.average_actor_loss = 0.0
self.average_critic_loss = 0.0
# Aliases for convenience
self.q_function = self.model['q_function']
self.policy = self.model['policy']
self.target_q_function = self.target_model['q_function']
self.target_policy = self.target_model['policy']
self.sync_target_network()
def sync_target_network(self):
"""Synchronize target network with current network."""
synchronize_parameters(
src=self.model,
dst=self.target_model,
method=self.target_update_method,
tau=self.soft_update_tau)
def update(self, experiences, errors_out=None):
"""Update the model from experiences."""
batch_size = len(experiences)
batch_exp = batch_experiences(
experiences,
xp=self.xp,
phi=self.phi,
gamma=self.gamma,
batch_states=self.batch_states,
)
batch_state = batch_exp['state']
batch_actions = batch_exp['action']
batch_next_state = batch_exp['next_state']
batch_rewards = batch_exp['reward']
batch_terminal = batch_exp['is_state_terminal']
batch_discount = batch_exp['discount']
# Update Q-function
def compute_critic_loss():
with chainer.no_backprop_mode():
pout = self.target_policy(batch_next_state)
next_actions = pout.sample()
next_q = self.target_q_function(batch_next_state, next_actions)
assert next_q.shape == (batch_size, 1)
target_q = (batch_rewards[..., None]
+ (batch_discount[..., None]
* (1.0 - batch_terminal[..., None])
* next_q))
assert target_q.shape == (batch_size, 1)
predict_q = self.q_function(batch_state, batch_actions)
assert predict_q.shape == (batch_size, 1)
loss = F.mean_squared_error(target_q, predict_q)
# Update stats
self.average_critic_loss *= self.average_loss_decay
self.average_critic_loss += ((1 - self.average_loss_decay) *
float(loss.array))
return loss
def compute_actor_loss():
pout = self.policy(batch_state)
sampled_actions = pout.sample().array
log_probs = pout.log_prob(sampled_actions)
with chainer.using_config('train', False):
q = self.q_function(batch_state, sampled_actions)
v = self.q_function(
batch_state, pout.most_probable)
advantage = F.reshape(q - v, (batch_size,))
advantage = chainer.Variable(advantage.array)
loss = - F.sum(advantage * log_probs + self.beta * pout.entropy) \
/ batch_size
# Update stats
self.average_actor_loss *= self.average_loss_decay
self.average_actor_loss += ((1 - self.average_loss_decay) *
float(loss.array))
return loss
self.critic_optimizer.update(compute_critic_loss)
self.actor_optimizer.update(compute_actor_loss)
def act_and_train(self, obs, reward):
self.logger.debug('t:%s r:%s', self.t, reward)
greedy_action = self.act(obs)
action = self.explorer.select_action(self.t, lambda: greedy_action)
self.t += 1
# Update the target network
if self.t % self.target_update_interval == 0:
self.sync_target_network()
if self.last_state is not None:
assert self.last_action is not None
# Add a transition to the replay buffer
self.replay_buffer.append(
state=self.last_state,
action=self.last_action,
reward=reward,
next_state=obs,
next_action=action,
is_state_terminal=False)
self.last_state = obs
self.last_action = action
self.replay_updater.update_if_necessary(self.t)
return self.last_action
def act(self, obs):
with chainer.using_config('train', False):
s = self.batch_states([obs], self.xp, self.phi)
if self.act_deterministically:
action = self.policy(s).most_probable
else:
action = self.policy(s).sample()
# Q is not needed here, but log it just for information
q = self.q_function(s, action)
# Update stats
self.average_q *= self.average_q_decay
self.average_q += (1 - self.average_q_decay) * float(q.array)
self.logger.debug('t:%s a:%s q:%s',
self.t, action.array[0], q.array)
return cuda.to_cpu(action.array[0])
def stop_episode_and_train(self, state, reward, done=False):
assert self.last_state is not None
assert self.last_action is not None
# Add a transition to the replay buffer
self.replay_buffer.append(
state=self.last_state,
action=self.last_action,
reward=reward,
next_state=state,
next_action=self.last_action,
is_state_terminal=done)
self.stop_episode()
def stop_episode(self):
self.last_state = None
self.last_action = None
if isinstance(self.model, Recurrent):
self.model.reset_state()
self.replay_buffer.stop_current_episode()
def select_action(self, state):
return self.explorer.select_action(
self.t, lambda: self.act(state))
def get_statistics(self):
return [
('average_q', self.average_q),
('average_actor_loss', self.average_actor_loss),
('average_critic_loss', self.average_critic_loss),
]