class Runner(object):
"""Actor object to start running simulation on workers.
The gradient computation is also executed from this object.
"""
def __init__(self, env_name, actor_id, logdir="/tmp/ray/a3c/", start=True):
env = create_env(env_name)
self.id = actor_id
num_actions = env.action_space.n
self.policy = LSTMPolicy(env.observation_space.shape, num_actions,
actor_id)
self.runner = RunnerThread(env, self.policy, 20)
self.env = env
self.logdir = logdir
if start:
self.start()
def pull_batch_from_queue(self):
"""Take a rollout from the queue of the thread runner."""
rollout = self.runner.queue.get(timeout=600.0)
if isinstance(rollout, BaseException):
raise rollout
while not rollout.terminal:
try:
part = self.runner.queue.get_nowait()
if isinstance(part, BaseException):
raise rollout
rollout.extend(part)
except queue.Empty:
break
return rollout
def get_completed_rollout_metrics(self):
"""Returns metrics on previously completed rollouts.
Calling this clears the queue of completed rollout metrics.
"""
completed = []
while True:
try:
completed.append(self.runner.metrics_queue.get_nowait())
except queue.Empty:
break
return completed
def start(self):
summary_writer = tf.summary.FileWriter(
os.path.join(self.logdir, "agent_%d" % self.id))
self.summary_writer = summary_writer
self.runner.start_runner(self.policy.sess, summary_writer)
def compute_gradient(self, params):
self.policy.set_weights(params)
rollout = self.pull_batch_from_queue()
batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)
gradient = self.policy.get_gradients(batch)
info = {"id": self.id, "size": len(batch.a)}
return gradient, info
def __init__(self, env_name, policy_cls, actor_id, batch_size, logdir):
env = create_env(env_name)
self.id = actor_id
# TODO(rliaw): should change this to be just env.observation_space
self.policy = policy_cls(env.observation_space.shape, env.action_space)
self.runner = RunnerThread(env, self.policy, batch_size)
self.env = env
self.logdir = logdir
self.start()
def __init__(self, env_name, actor_id, logdir="/tmp/ray/a3c/", start=True):
env = create_env(env_name)
self.id = actor_id
num_actions = env.action_space.n
self.policy = LSTMPolicy(env.observation_space.shape, num_actions,
actor_id)
self.runner = RunnerThread(env, self.policy, 20)
self.env = env
self.logdir = logdir
if start:
self.start()
class Runner(object):
"""Actor object to start running simulation on workers.
The gradient computation is also executed from this object.
"""
def __init__(self, env_name, policy_cls, actor_id, batch_size, logdir):
env = create_env(env_name)
self.id = actor_id
# TODO(rliaw): should change this to be just env.observation_space
self.policy = policy_cls(env.observation_space.shape, env.action_space)
self.runner = RunnerThread(env, self.policy, batch_size)
self.env = env
self.logdir = logdir
self.start()
def pull_batch_from_queue(self):
"""Take a rollout from the queue of the thread runner."""
rollout = self.runner.queue.get(timeout=600.0)
if isinstance(rollout, BaseException):
raise rollout
while not rollout.terminal:
try:
part = self.runner.queue.get_nowait()
if isinstance(part, BaseException):
raise rollout
rollout.extend(part)
except queue.Empty:
break
return rollout
def get_completed_rollout_metrics(self):
"""Returns metrics on previously completed rollouts.
Calling this clears the queue of completed rollout metrics.
"""
completed = []
while True:
try:
completed.append(self.runner.metrics_queue.get_nowait())
except queue.Empty:
break
return completed
def start(self):
summary_writer = tf.summary.FileWriter(
os.path.join(self.logdir, "agent_%d" % self.id))
self.summary_writer = summary_writer
self.runner.start_runner(self.policy.sess, summary_writer)
def compute_gradient(self, params):
self.policy.set_weights(params)
rollout = self.pull_batch_from_queue()
batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)
gradient, info = self.policy.get_gradients(batch)
if "summary" in info:
self.summary_writer.add_summary(
tf.Summary.FromString(info['summary']),
self.policy.local_steps)
self.summary_writer.flush()
info = {"id": self.id, "size": len(batch.a)}
return gradient, info