def __init__(self, env, policy_net, summary_writer, saver=None):
self.global_policy_net = policy_net
self.summary_writer = summary_writer
self.saver = saver
self.env = env
# Correct the path
self.checkpoint_path = os.path.abspath(
os.path.join(summary_writer.get_logdir(), "../checkpoints/model"))
print('[PM] checkpoint_path: {}'.format(self.checkpoint_path))
# Local policy net
with tf.variable_scope("policy_eval"):
if LSTM_POLICY:
self.policy_net = LSTMPolicyEstimator(policy_net.num_outputs)
else:
self.policy_net = PolicyEstimator(policy_net.num_outputs)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope="policy_eval",
collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def __init__(self, env, policy_net, summary_writer, saver=None):
self.global_policy_net = policy_net
self.summary_writer = summary_writer
self.saver = saver
#self.sp = StateProcessor()
self.env = CDLL('./PythonAccessToSim.so')
self.env.step.restype = step_result
self.env.send_command.restype = c_int
self.env.initialize.restype = c_int
self.env.recieve_state_gui.restype = step_result
self.actions = list(range(0,3*Num_Targets))
self.checkpoint_path = os.path.abspath(os.path.join(summary_writer.get_logdir(), "./checkpoints/model"))
print(self.checkpoint_path)
# Local policy net
with tf.variable_scope("policy_eval"):
self.policy_net = PolicyEstimator(policy_net.num_outputs)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(scope="policy_eval", collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def testPredict(self):
env = make_env()
sp = StateProcessor()
estimator = PolicyEstimator(len(VALID_ACTIONS))
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
# Generate a state
state = sp.process(env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
processed_states = np.array([processed_state])
# Run feeds
feed_dict = {
estimator.states: processed_states,
estimator.targets: [1.0],
estimator.actions: [1]
}
loss = sess.run(estimator.loss, feed_dict)
pred = sess.run(estimator.predictions, feed_dict)
# Assertions
self.assertTrue(loss != 0.0)
self.assertEqual(pred["probs"].shape, (1, len(VALID_ACTIONS)))
self.assertEqual(pred["logits"].shape, (1, len(VALID_ACTIONS)))
def testGradient(self):
env = make_env()
sp = StateProcessor()
estimator = PolicyEstimator(len(VALID_ACTIONS))
grads = [g for g, _ in estimator.grads_and_vars]
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
# Generate a state
state = sp.process(env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
processed_states = np.array([processed_state])
# Run feeds to get gradients
feed_dict = {
estimator.states: processed_states,
estimator.targets: [1.0],
estimator.actions: [1]
}
grads_ = sess.run(grads, feed_dict)
# Apply calculated gradients
grad_feed_dict = {k: v for k, v in zip(grads, grads_)}
_ = sess.run(estimator.train_op, grad_feed_dict)
def __init__(self, env, policy_net, summary_writer, saver=None):
self.video_dir = os.path.join(summary_writer.get_logdir(), "../videos")
self.video_dir = os.path.abspath(self.video_dir)
self.env = Monitor(env, directory=self.video_dir, video_callable=lambda x: True, resume=True)
self.global_policy_net = policy_net
self.summary_writer = summary_writer
self.saver = saver
self.sp = StateProcessor()
self.checkpoint_path = os.path.abspath(os.path.join(summary_writer.get_logdir(), "../checkpoints/model"))
try:
os.makedirs(self.video_dir)
except FileExistsError:
pass
# Local policy net
with tf.variable_scope("policy_eval"):
self.policy_net = PolicyEstimator(policy_net.num_outputs)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(scope="policy_eval", collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def __init__(self, env, policy_net, summary_writer, saver=None):
self.video_dir = os.path.join(summary_writer.get_logdir(), "../videos")
self.video_dir = os.path.abspath(self.video_dir)
self.env = Monitor(env,
directory=self.video_dir,
video_callable=lambda x: True,
resume=True)
self.global_policy_net = policy_net
self.summary_writer = summary_writer
self.saver = saver
self.checkpoint_path = os.path.abspath(
os.path.join(summary_writer.get_logdir(), "../checkpoints/model"))
try:
os.makedirs(self.video_dir)
except OSError as e:
# FileExistsError was added in Python 3.3; You can't use FileExistsError.
# https: // stackoverflow.com / questions / 20790580 / python - specifically - handle - file - exists - exception
# Use errno.EEXIST
pass
# Local policy net
with tf.variable_scope("policy_eval"):
self.policy_net = PolicyEstimator(policy_net.num_outputs)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope="policy_eval",
collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def __init__(self, name, env, policy_net, value_net, global_counter, discount_factor=0.99, summary_writer=None, max_global_steps=None):
self.name = name
self.discount_factor = discount_factor
self.max_global_steps = max_global_steps
self.global_step = tf.contrib.framework.get_global_step()
self.global_policy_net = policy_net
self.global_value_net = value_net
self.global_counter = global_counter
self.local_counter = itertools.count()
self.summary_writer = summary_writer
self.env = env
# Create local policy/value nets that are not updated asynchronously
with tf.variable_scope(name):
self.policy_net = PolicyEstimator(policy_net.num_outputs)
self.value_net = ValueEstimator(reuse=True)
# Op to copy params from global policy/valuenets
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(scope=self.name+'/', collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.vnet_train_op = make_train_op(self.value_net, self.global_value_net)
self.pnet_train_op = make_train_op(self.policy_net, self.global_policy_net)
self.state = None
def main():
VALID_ACTIONS = list(range(10))
MODEL_DIR = "data/train"
CHECKPOINT_DIR = os.path.join(MODEL_DIR, "checkpoints")
env = simulator.Task(debug_flag=True, test_flag=True, state_blink=True, state_inaccurate=True)
with tf.variable_scope("global") as vs:
policy_net = PolicyEstimator(num_outputs=len(VALID_ACTIONS))
saver = tf.train.Saver(keep_checkpoint_every_n_hours=2.0, max_to_keep=10)
pe = PolicyMonitor(
env=env,
policy_net=policy_net)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
# Load a previous checkpoint if it exists
latest_checkpoint = tf.train.latest_checkpoint(CHECKPOINT_DIR)
if latest_checkpoint:
print("Loading model checkpoint: {}".format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
else:
print("Fail to load model")
return
with sess.as_default(), sess.graph.as_default():
_ = sess.run(pe.copy_params_op)
done = False
state = np.stack([pe.env.reset().reshape(93,93)] * 4, axis=2)
rnn_state = pe.policy_net.state_init
total_reward = 0.0
num_episode = 0
while(1):
action_probs, next_rnn_state = pe._policy_net_predict(state, rnn_state, sess)
action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
next_state, reward, done = pe.env.step(action)
next_state = np.append(state[:,:,1:], np.expand_dims(next_state.reshape(93,93), 2), axis=2)
total_reward += reward
state = next_state
rnn_state = next_rnn_state
if done == True: # done was True in latest transition; we have already stored that
done = False
state = np.stack([pe.env.reset().reshape(93,93)] * 4, axis=2)
rnn_state = pe.policy_net.state_init
total_reward = 0.0
num_episode = num_episode + 1
cv2.waitKey(30)
if num_episode >= 10:
break;
def setUp(self):
super(PolicyMonitorTest, self).setUp()
self.env = make_env()
self.global_step = tf.Variable(0, name="global_step", trainable=False)
self.summary_writer = tf.train.SummaryWriter(tempfile.mkdtemp())
with tf.variable_scope("global") as vs:
self.global_policy_net = PolicyEstimator(len(VALID_ACTIONS))
self.global_value_net = ValueEstimator(reuse=True)
def __init__(self, env, policy_net):
self.env = env
self.global_policy_net = policy_net
# Local policy net
with tf.variable_scope("policy_eval"):
self.policy_net = PolicyEstimator(policy_net.num_outputs)
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(scope="policy_eval", collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def setUp(self):
super(WorkerTest, self).setUp()
self.env = make_env()
self.discount_factor = 0.99
self.global_step = tf.Variable(0, name="global_step", trainable=False)
self.global_counter = itertools.count()
self.sp = StateProcessor()
with tf.variable_scope("global") as vs:
self.global_policy_net = PolicyEstimator(len(VALID_ACTIONS))
self.global_value_net = ValueEstimator(reuse=True)
def __init__(self,
name,
env,
env_id,
curriculum,
policy_nets,
value_nets,
shared_final_layer,
global_counter,
discount_factor=0.99,
summary_writer=None,
max_global_steps=None):
self.name = name
self.discount_factor = discount_factor
self.max_global_steps = max_global_steps
self.global_step = tf.contrib.framework.get_global_step()
self.global_policy_nets = policy_nets
self.global_value_nets = value_nets
self.global_counter = global_counter
self.local_counter = itertools.count()
self.env = env
self.env_id = env_id
self.curriculum = curriculum
self.shared_final_layer = shared_final_layer
# Create local policy/value nets that are not updated asynchronously
with tf.variable_scope(name):
self.policy_net = PolicyEstimator(policy_nets[0].num_outputs,
state_dims=env.get_state_size())
self.value_net = ValueEstimator(reuse=True,
state_dims=env.get_state_size())
# Op to copy params from global policy/valuenets
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global_{}".format(env_id),
collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope=self.name, collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.vnet_train_op = self.make_train_op(self.value_net,
self.global_value_nets)
self.pnet_train_op = self.make_train_op(self.policy_net,
self.global_policy_nets)
if self.shared_final_layer:
# create ops to train the final layers of all other agents
self.policy_layer_train_ops = self.make_final_layer_train_ops(
self.policy_net, self.global_policy_nets, 'policy')
self.value_layer_train_ops = self.make_final_layer_train_ops(
self.value_net, self.global_value_nets, 'value')
self.state = None
self.epochs = 0
def setUp(self):
super(WorkerTest, self).setUp()
self.env = make_env()
self.discount_factor = 0.99
self.global_step = tf.Variable(0, name="global_step", trainable=False)
self.global_counter = itertools.count()
self.sp = StateProcessor()
with tf.variable_scope("global") as vs:
print("length of the actions: {}".format(len(VALID_ACTIONS)))
self.global_policy_net = PolicyEstimator(len(VALID_ACTIONS))
self.global_value_net = ValueEstimator(reuse=True)
def testPolicyNetPredict(self):
w = Worker(name="test",
env=make_env(),
policy_net=self.global_policy_net,
value_net=self.global_value_net,
global_counter=self.global_counter,
discount_factor=self.discount_factor)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
state = self.sp.process(self.env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
action_values = w._policy_net_predict(processed_state, sess)
self.assertEqual(action_values.shape, (4, ))
def testValueNetPredict(self):
w = Worker(name="test",
env=make_env(),
policy_net=self.global_policy_net,
value_net=self.global_value_net,
global_counter=self.global_counter,
discount_factor=self.discount_factor)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
state = self.sp.process(self.env.reset())
processed_state = atari_helpers.atari_make_initial_state(state)
w.state = processed_state
transitions, local_t, global_t = w.run_n_steps(10, sess)
policy_net_loss, value_net_loss, policy_net_summaries, value_net_summaries = w.update(
transitions, sess)
self.assertEqual(len(transitions), 10)
self.assertIsNotNone(policy_net_loss)
self.assertIsNotNone(value_net_loss)
self.assertIsNotNone(policy_net_summaries)
self.assertIsNotNone(value_net_summaries)
def __init__(self, env, policy_net, task):
self.env = env
self.global_policy_net = policy_net
self.task = task
# Local policy net
with tf.variable_scope("policy_visualization"):
self.policy_net = PolicyEstimator(policy_net.num_outputs, state_dims=self.env.get_state_size())
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(scope="policy_visualization", collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def __init__(self,
name,
env,
policy_net,
value_net,
global_counter,
discount_factor=0.99,
summary_writer=None,
max_global_steps=None):
self.name = name
self.discount_factor = discount_factor
self.max_global_steps = max_global_steps
self.global_step = tf.contrib.framework.get_global_step()
self.global_policy_net = policy_net
self.global_value_net = value_net
self.global_counter = global_counter
self.local_counter = itertools.count()
#self.sp = StateProcessor()
self.summary_writer = summary_writer
self.env = CDLL('./PythonAccessToSim.so')
self.env.step.restype = step_result
self.env.send_command.restype = c_int
self.env.initialize.restype = c_int
self.env.recieve_state_gui.restype = step_result
self.actions = list(range(0, 3 * Num_Targets))
# Create local policy/value nets that are not updated asynchronously
with tf.variable_scope(name):
self.policy_net = PolicyEstimator(policy_net.num_outputs)
self.value_net = ValueEstimator(reuse=True)
# Op to copy params from global policy/valuenets
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope=self.name, collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.vnet_train_op = make_train_op(self.value_net,
self.global_value_net)
self.pnet_train_op = make_train_op(self.policy_net,
self.global_policy_net)
self.state = None
self.sim = None
def __init__(self,
env,
env_id,
curriculum,
policy_net,
saver=None,
n_eval=10,
logfile=None,
checkpoint_path=None):
self.env = env
self.env_id = env_id
self.curriculum = curriculum
self.global_policy_net = policy_net
self.saver = saver
self.n_eval = n_eval
self.checkpoint_path = checkpoint_path
self.logger = logging.getLogger('eval runs {}'.format(env_id))
hdlr = logging.FileHandler(logfile)
formatter = logging.Formatter(
'[%(asctime)s] [%(levelname)s] %(message)s')
hdlr.setFormatter(formatter)
self.logger.addHandler(hdlr)
self.logger.setLevel(logging.INFO)
# Local policy net
with tf.variable_scope("policy_eval_{}".format(env_id)):
self.policy_net = PolicyEstimator(
policy_net.num_outputs, state_dims=self.env.get_state_size())
#Directory to save checkpoints to. Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global_{}".format(env_id),
collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope="policy_eval_{}".format(env_id),
collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.epochs = 0
def __init__(self, env, policy_net, summary_writer, saver=None):
self.env = env
self.global_policy_net = policy_net
self.summary_writer = summary_writer
self.saver = saver
self.counter = 0
self.checkpoint_path = os.path.abspath(
os.path.join(summary_writer.get_logdir(), "../checkpoints/model"))
# Local policy net
with tf.variable_scope("policy_eval"):
self.policy_net = PolicyEstimator(policy_net.num_outputs,
policy_net.observation_space)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope="policy_eval",
collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def main():
# Depending on the game we may have a limited action space
env_ = gym.make(FLAGS.env)
num_actions = env_.action_space.n
dim_obs = list(env_.observation_space.shape)
assert len(dim_obs) == 3 and dim_obs[2] == 3 #make sure it is a RGB frame
N_FRAME = FLAGS.n_frame if FLAGS.n_frame else 1
dim_obs[2] *= N_FRAME
print("Valid number of actions is {}".format(num_actions))
print("The dimension of the observation space is {}".format(dim_obs))
env_.close()
# Set the number of workers
NUM_WORKERS = (FLAGS.parallelism
if FLAGS.parallelism else multiprocessing.cpu_count())
MODEL_DIR = FLAGS.model_dir
CP_H = FLAGS.checkpoint_hour
CHECKPOINT_DIR = os.path.join(MODEL_DIR, "checkpoints")
TENSORBOARD_DIR = os.path.join(MODEL_DIR, "tb")
# Optionally empty model directory
if FLAGS.reset:
shutil.rmtree(MODEL_DIR, ignore_errors=True)
if not os.path.exists(CHECKPOINT_DIR):
os.makedirs(CHECKPOINT_DIR)
summary_writer = tf.summary.FileWriter(TENSORBOARD_DIR)
with tf.device("/cpu:0"):
# Keeps track of the number of updates we've performed
global_step = tf.Variable(0, name="global_step", trainable=False)
# Global policy and value nets
with tf.variable_scope("global") as vs:
policy_net = PolicyEstimator(num_outputs=num_actions,
dim_inputs=dim_obs)
value_net = ValueEstimator(reuse=True, dim_inputs=dim_obs)
# Global step iterator
global_counter = itertools.count()
# Create worker graphs
workers = []
for worker_id in range(NUM_WORKERS):
# We only write summaries in one of the workers because they're
# pretty much identical and writing them on all workers
# would be a waste of space
worker_summary_writer = None
if worker_id == 0:
worker_summary_writer = summary_writer
worker = Worker(name="worker_{}".format(worker_id),
env=gym.make(FLAGS.env),
policy_net=policy_net,
value_net=value_net,
global_counter=global_counter,
discount_factor=0.99,
summary_writer=worker_summary_writer,
max_global_steps=FLAGS.max_global_steps,
n_frame=N_FRAME)
workers.append(worker)
saver = tf.train.Saver(keep_checkpoint_every_n_hours=CP_H,
max_to_keep=10)
# Used to occasionally save videos for our policy net
# and write episode rewards to Tensorboard
pe = PolicyMonitor(env=gym.make(FLAGS.env),
policy_net=policy_net,
summary_writer=summary_writer,
saver=saver)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
# Load a previous checkpoint if it exists
latest_checkpoint = tf.train.latest_checkpoint(CHECKPOINT_DIR)
if latest_checkpoint:
print("Loading model checkpoint: {}".format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
# Start worker threads
worker_threads = []
for worker in workers:
print("starting worker:")
worker_fn = lambda: worker.run(sess, coord, FLAGS.t_max)
t = threading.Thread(target=worker_fn)
t.start()
worker_threads.append(t)
# Start a thread for policy eval task
monitor_thread = threading.Thread(
target=lambda: pe.continuous_eval(FLAGS.eval_every, sess, coord))
monitor_thread.start()
# Wait for all workers to finish
coord.join(worker_threads)
shutil.rmtree(MODEL_DIR, ignore_errors=True)
if not os.path.exists(CHECKPOINT_DIR):
os.makedirs(CHECKPOINT_DIR)
summary_writer = tf.summary.FileWriter(os.path.join(MODEL_DIR, "train"))
with tf.device("/cpu:0"):
# Keeps track of the number of updates we've performed
global_step = tf.Variable(0, name="global_step", trainable=False)
# Global policy and value nets
with tf.variable_scope("global") as vs:
policy_net = PolicyEstimator(num_outputs=len(VALID_ACTIONS),
observation_space=env_.observation_len)
value_net = ValueEstimator(observation_space=env_.observation_len,
reuse=True)
# Global step iterator
global_counter = itertools.count()
# Create worker graphs
workers = []
for worker_id in range(NUM_WORKERS):
# We only write summaries in one of the workers because they're
# pretty much identical and writing them on all workers
# would be a waste of space
worker_summary_writer = None
if worker_id == 0:
CHECKPOINT_DIR = os.path.join(MODEL_DIR, "checkpoints")
if FLAGS.reset:
shutil.rmtree(MODEL_DIR, ignore_errors=True)
if not os.path.exists(CHECKPOINT_DIR):
os.makedirs(CHECKPOINT_DIR)
summary_writer = tf.summary.FileWriter(os.path.join(MODEL_DIR, "train"))
with tf.device("/cpu/0"):
global_step = tf.Variable(0, name="globa_step", trainable=False)
with tf.variable_scope("global") as vs:
policy_net = PolicyEstimator(num_outputs=len(VALID_ACTIONS))
value_net = ValueEstimator(reuse=True)
global_counter = itertools.count()
workers = []
for worker_id in range(NUM_WORKERS):
worker_summary_writer = None
if worker_id == 0:
worker_summary_writer = summary_writer
worker = Worker(name="worker_{}".format(worker_id),
env=make_env(),
policy_net=policy_net,
value_net=value_net,
def __init__(self,
name,
envs,
policy_net,
value_net,
global_counter,
domain,
instances,
discount_factor=0.99,
summary_writer=None,
max_global_steps=None):
self.name = name
self.domain = domain
self.instances = instances
self.dropout = 0.0
self.discount_factor = discount_factor
self.max_global_steps = max_global_steps
self.global_step = tf.contrib.framework.get_global_step()
self.global_policy_net = policy_net
self.global_value_net = value_net
self.global_counter = global_counter
self.local_counter = itertools.count()
self.summary_writer = summary_writer
self.envs = envs
self.n = self.envs[0].num_state_vars
self.N = len(instances)
self.current_instance = 0
assert (policy_net.num_inputs == value_net.num_inputs)
assert (self.N == len(self.envs))
self.num_inputs = policy_net.num_inputs
# Construct adjacency lists
self.adjacency_lists = [None] * self.N
self.nf_features = [None] * self.N
self.single_adj_preprocessed_list = [None] * self.N
for i in range(self.N):
self.instance_parser = InstanceParser(self.domain,
self.instances[i])
self.fluent_feature_dims, self.nonfluent_feature_dims = self.instance_parser.get_feature_dims(
)
self.nf_features[i] = self.instance_parser.get_nf_features()
adjacency_list = self.instance_parser.get_adjacency_list()
self.adjacency_lists[i] = nx.adjacency_matrix(
nx.from_dict_of_lists(adjacency_list))
self.single_adj_preprocessed_list[i] = preprocess_adj(
self.adjacency_lists[i])
# Create local policy/value nets that are not updated asynchronously
with tf.variable_scope(name):
self.policy_net = PolicyEstimator(
policy_net.num_inputs, self.N, policy_net.num_hidden1,
policy_net.num_hidden2, policy_net.num_outputs,
policy_net.fluent_feature_dims,
policy_net.nonfluent_feature_dims, policy_net.activation,
policy_net.learning_rate)
self.value_net = ValueEstimator(
value_net.num_inputs, self.N, value_net.num_hidden1,
value_net.num_hidden2, value_net.fluent_feature_dims,
value_net.nonfluent_feature_dims, value_net.activation,
value_net.learning_rate)
# Op to copy params from global policy/valuenets
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope=self.name, collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.vnet_train_op_list = [None] * self.N
self.pnet_train_op_list = [None] * self.N
for i in range(self.N):
self.vnet_train_op_list[i] = make_train_op(
self.value_net, self.global_value_net, i)
self.pnet_train_op_list[i] = make_train_op(
self.policy_net, self.global_policy_net, i)
self.state = None
os.path.join(MODEL_DIR, "val_summaries"))
# Keeps track of the number of updates we've performed
global_step = tf.Variable(0, name="global_step", trainable=False)
global_learning_rate = FLAGS.lr
# Global policy and value nets
with tf.variable_scope("global") as vs:
policy_net = PolicyEstimator(num_nodes_list=num_nodes_list,
fluent_feature_dims=fluent_feature_dims,
nonfluent_feature_dims=nonfluent_feature_dims,
N=FLAGS.num_instances,
num_valid_actions_list=num_valid_actions_list,
action_details_list=action_details_list,
num_graph_fluent_list=num_graph_fluent_list,
num_gcn_hidden=policy_num_gcn_hidden,
num_action_dim=FLAGS.num_action_dim,
num_decoder_dim=FLAGS.num_decoder_dim,
num_adjacency_list=num_adjacency_list,
num_gat_layers=FLAGS.num_gat_layers,
activation=FLAGS.activation,
learning_rate=global_learning_rate)
value_net = ValueEstimator(num_nodes_list=num_nodes_list,
fluent_feature_dims=fluent_feature_dims,
nonfluent_feature_dims=nonfluent_feature_dims,
N=FLAGS.num_instances,
num_graph_fluent_list=num_graph_fluent_list,
num_gcn_hidden=value_num_gcn_hidden,
num_action_dim=FLAGS.num_action_dim,
num_decoder_dim=FLAGS.num_decoder_dim,
actor_lr*=.999
break
with tf.Session() as sess:
env = gym.envs.make("CartPole-v1")
seed = FLAGS.seed
np.random.seed(seed)
env.seed(seed)
tf.set_random_seed(seed)
print('seed: {} '.format(seed))
policy_estimator = PolicyEstimator(env)
value_estimator = ValueEstimator(env)
sess.run(tf.global_variables_initializer())
actor_critic(sess, env, policy_estimator,
value_estimator, FLAGS.num_episodes)
if not os.path.exists(CHECKPOINT_DIR):
os.makedirs(CHECKPOINT_DIR)
if not os.path.exists(LOG_DIR):
os.makedirs(LOG_DIR)
with tf.device("/cpu:0"):
# Keeps track of the number of updates we've performed
global_step = tf.Variable(0, name="global_step", trainable=False)
# different policy and value nets for all tasks
policy_nets = []
value_nets = []
for e in range(len(envs)):
with tf.variable_scope("global_{}".format(e)) as vs:
policy_nets.append(PolicyEstimator(
num_outputs=len(VALID_ACTIONS), state_dims=envs[e].get_state_size()))
value_nets.append(ValueEstimator(
reuse=True, state_dims=envs[e].get_state_size()))
if FLAGS.shared_final_layer:
# make all final layer weights the same
initial_copy_ops = []
for e in range(1, len(envs)):
initial_copy_ops += make_copy_params_op(
tf.contrib.slim.get_variables(scope="global_0/policy_net", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(scope="global_{}/policy_net".format(e), collection=tf.GraphKeys.TRAINABLE_VARIABLES))
initial_copy_ops += make_copy_params_op(
tf.contrib.slim.get_variables(scope="global_0/value_net", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(scope="global_{}/value_net".format(e), collection=tf.GraphKeys.TRAINABLE_VARIABLES))
# Global step iterator
global_counter = itertools.count()
def __init__(self,
envs,
policy_net,
domain,
instances,
neighbourhood,
summary_writer,
saver=None):
self.stats_dir = os.path.join(summary_writer.get_logdir(), "../stats")
self.stats_dir = os.path.abspath(self.stats_dir)
self.domain = domain
self.instances = instances
self.N = len(instances)
self.num_nodes_list = policy_net.num_nodes_list
self.num_adjacency_list = policy_net.num_adjacency_list
self.envs = envs
self.global_policy_net = policy_net
# Construct adjacency list
self.adjacency_lists = [None] * self.N
self.nf_features = [None] * self.N
self.adjacency_lists_with_biases = [None] * self.N
for i in range(self.N):
self.fluent_feature_dims, self.nonfluent_feature_dims = self.envs[
i].get_feature_dims()
self.nf_features[i] = self.envs[i].get_nf_features()
adjacency_list = self.envs[i].get_adjacency_list()
self.adjacency_lists[i] = [
get_adj_mat_from_list(aj) for aj in adjacency_list
]
self.adjacency_lists_with_biases[i] = [
process.adj_to_bias(np.array([aj]), [self.num_nodes_list[i]],
nhood=neighbourhood)[0]
for aj in self.adjacency_lists[i]
]
self.summary_writer = summary_writer
self.saver = saver
self.checkpoint_path = os.path.abspath(
os.path.join(summary_writer.get_logdir(), "../checkpoints/model"))
try:
os.makedirs(self.stats_dir)
except:
pass
# Local policy net
with tf.variable_scope("policy_eval"):
self.policy_net = PolicyEstimator(
policy_net.num_nodes_list, policy_net.fluent_feature_dims,
policy_net.nonfluent_feature_dims, policy_net.N,
policy_net.num_valid_actions_list,
policy_net.action_details_list,
policy_net.num_graph_fluent_list, policy_net.num_gcn_hidden,
policy_net.num_action_dim, policy_net.num_decoder_dim,
policy_net.num_adjacency_list, policy_net.num_gat_layers,
policy_net.activation, policy_net.learning_rate)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope="policy_eval",
collection=tf.GraphKeys.TRAINABLE_VARIABLES))
class PolicyMonitor(object):
"""
Helps evaluating a policy by running an episode in an environment,
saving a video, and plotting summaries to Tensorboard.
Args:
env: environment to run in
policy_net: A policy estimator
summary_writer: a tf.train.SummaryWriter used to write Tensorboard summaries
"""
def __init__(self, env, policy_net, summary_writer, saver=None):
self.global_policy_net = policy_net
self.summary_writer = summary_writer
self.saver = saver
self.env = env
# Correct the path
self.checkpoint_path = os.path.abspath(
os.path.join(summary_writer.get_logdir(), "../checkpoints/model"))
print('[PM] checkpoint_path: {}'.format(self.checkpoint_path))
# Local policy net
with tf.variable_scope("policy_eval"):
if LSTM_POLICY:
self.policy_net = LSTMPolicyEstimator(policy_net.num_outputs)
else:
self.policy_net = PolicyEstimator(policy_net.num_outputs)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope="policy_eval",
collection=tf.GraphKeys.TRAINABLE_VARIABLES))
def eval_once(self, sess):
with sess.as_default(), sess.graph.as_default():
# Copy params to local model
global_step, _ = sess.run(
[tf.contrib.framework.get_global_step(), self.copy_params_op])
# Run an episode
done = False
state = self.env.reset()
if LSTM_POLICY:
self.policy_net.reset_lstm_features()
total_reward = 0.0
episode_length = 0
while not done:
if LSTM_POLICY:
action_probs = self.policy_net.action_inference(state)
else:
action_probs = self.policy_net.action_prediction(state)
action = np.random.choice(np.arange(len(action_probs)),
p=action_probs)
next_state, reward, done, _ = self.env.step(action)
# next_state = atari_helpers.atari_make_next_state(state, self.sp.process(next_state))
total_reward += reward
episode_length += 1
state = next_state
# Add summaries
episode_summary = tf.Summary()
episode_summary.value.add(simple_value=total_reward,
tag="eval/total_reward")
episode_summary.value.add(simple_value=episode_length,
tag="eval/episode_length")
self.summary_writer.add_summary(episode_summary, global_step)
self.summary_writer.flush()
if self.saver is not None:
self.saver.save(sess, self.checkpoint_path)
tf.logging.info(
"Eval results at step {}: total_reward {}, episode_length {}".
format(global_step, total_reward, episode_length))
return total_reward, episode_length
def continuous_eval(self, eval_every, sess, coord):
"""
Continuously evaluates the policy every [eval_every] seconds.
"""
try:
while not coord.should_stop():
self.eval_once(sess)
# Sleep until next evaluation cycle
time.sleep(eval_every)
except tf.errors.CancelledError:
return
def __init__(self,
name,
envs,
policy_net,
value_net,
global_counter,
domain,
instances,
N_train,
neighbourhood,
discount_factor=0.99,
summary_writer=None,
max_global_steps=None,
train_policy=True):
self.name = name
self.domain = domain
self.instances = instances
self.dropout = 0.0
self.discount_factor = discount_factor
self.max_global_steps = max_global_steps
self.global_step = tf.train.get_global_step()
self.global_policy_net = policy_net
self.global_value_net = value_net
self.global_counter = global_counter
self.local_counter = itertools.count()
self.summary_writer = summary_writer
self.envs = envs
self.num_adjacency_list = policy_net.num_adjacency_list
self.N = len(instances)
self.N_train = N_train
self.current_instance = 0
assert (self.N == len(self.envs))
self.num_nodes_list = policy_net.num_nodes_list
self.train_policy = train_policy
# Construct adjacency lists
self.adjacency_lists = [None] * self.N
self.nf_features = [None] * self.N
self.adjacency_lists_with_biases = [None] * self.N
for i in range(self.N):
self.fluent_feature_dims, self.nonfluent_feature_dims = self.envs[
i].get_feature_dims()
self.nf_features[i] = self.envs[i].get_nf_features()
adjacency_list = self.envs[i].get_adjacency_list()
self.adjacency_lists[i] = [
get_adj_mat_from_list(aj) for aj in adjacency_list
]
self.adjacency_lists_with_biases[i] = [
process.adj_to_bias(
np.array([aj]), [self.num_nodes_list[i]],
nhood=neighbourhood)[0] for aj in self.adjacency_lists[i]
]
# Create local policy/value nets that are not updated asynchronously
with tf.variable_scope(name):
self.policy_net = PolicyEstimator(
policy_net.num_nodes_list, policy_net.fluent_feature_dims,
policy_net.nonfluent_feature_dims, policy_net.N,
policy_net.num_valid_actions_list,
policy_net.action_details_list,
policy_net.num_graph_fluent_list, policy_net.num_gcn_hidden,
policy_net.num_action_dim, policy_net.num_decoder_dim,
policy_net.num_adjacency_list, policy_net.num_gat_layers,
policy_net.activation, policy_net.learning_rate)
self.value_net = ValueEstimator(
value_net.num_nodes_list, value_net.fluent_feature_dims,
value_net.nonfluent_feature_dims, self.N,
value_net.num_graph_fluent_list, value_net.num_gcn_hidden,
value_net.num_action_dim, value_net.num_decoder_dim,
value_net.num_adjacency_list, value_net.num_gat_layers,
value_net.activation, value_net.learning_rate)
# Op to copy params from global policy/valuenets
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope=self.name, collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.vnet_train_op_list = [None] * self.N
if self.train_policy:
self.pnet_train_op_list = [None] * self.N
for i in range(self.N):
self.vnet_train_op_list[i] = make_train_op(
self.value_net, self.global_value_net, i)
if self.train_policy:
self.pnet_train_op_list[i] = make_train_op(
self.policy_net, self.global_policy_net, i)
self.state = None
self.start_time = time.time()
def __init__(self,
envs,
policy_net,
domain,
instances,
summary_writer,
saver=None):
self.stats_dir = os.path.join(summary_writer.get_logdir(), "../stats")
self.stats_dir = os.path.abspath(self.stats_dir)
self.n = envs[0].num_state_vars
self.domain = domain
self.instances = instances
self.N = len(instances)
self.envs = envs
self.global_policy_net = policy_net
# Construct adjacency list
self.adjacency_lists = [None] * self.N
self.single_adj_preprocessed_list = [None] * self.N
for i in range(self.N):
self.instance_parser = InstanceParser(self.domain,
self.instances[i])
self.fluent_feature_dims, self.nonfluent_feature_dims = self.instance_parser.get_feature_dims(
)
self.nf_features = self.instance_parser.get_nf_features()
adjacency_list = self.instance_parser.get_adjacency_list()
self.adjacency_lists[i] = nx.adjacency_matrix(
nx.from_dict_of_lists(adjacency_list))
self.single_adj_preprocessed_list[i] = preprocess_adj(
self.adjacency_lists[i])
self.summary_writer = summary_writer
self.saver = saver
self.checkpoint_path = os.path.abspath(
os.path.join(summary_writer.get_logdir(), "../checkpoints/model"))
try:
os.makedirs(self.stats_dir)
except:
pass
# Local policy net
with tf.variable_scope("policy_eval"):
self.policy_net = PolicyEstimator(
policy_net.num_inputs, policy_net.N, policy_net.num_hidden1,
policy_net.num_hidden2, policy_net.num_hidden_transition,
policy_net.num_outputs, policy_net.fluent_feature_dims,
policy_net.nonfluent_feature_dims, policy_net.activation,
policy_net.learning_rate)
# Op to copy params from global policy/value net parameters
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope="policy_eval",
collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.num_inputs = policy_net.num_inputs
if FLAGS.lr_decay:
global_learning_rate = tf.train.exponential_decay(FLAGS.lr,
global_step,
500000,
0.3,
staircase=True)
else:
global_learning_rate = FLAGS.lr
# Global policy and value nets
with tf.variable_scope("global") as vs:
policy_net = PolicyEstimator(num_inputs=num_inputs,
fluent_feature_dims=fluent_feature_dims,
nonfluent_feature_dims=nonfluent_feature_dims,
N=1,
num_hidden1=policy_num_hidden1,
num_hidden2=policy_num_hidden2,
num_outputs=num_valid_actions,
activation=FLAGS.activation,
learning_rate=global_learning_rate)
value_net = ValueEstimator(num_inputs=num_inputs,
fluent_feature_dims=fluent_feature_dims,
nonfluent_feature_dims=nonfluent_feature_dims,
N=1,
num_hidden1=value_num_hidden1,
num_hidden2=value_num_hidden2,
activation=FLAGS.activation,
learning_rate=global_learning_rate)
current_sa_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='global/policy_net/gconv1_vars')
class Worker(object):
"""
An A3C worker thread. Runs episodes locally and updates global shared value and policy nets.
Args:
name: A unique name for this worker
env: The Gym environment used by this worker
policy_net: Instance of the globally shared policy net
value_net: Instance of the globally shared value net
global_counter: Iterator that holds the global step
discount_factor: Reward discount factor
summary_writer: A tf.train.SummaryWriter for Tensorboard summaries
max_global_steps: If set, stop coordinator when global_counter > max_global_steps
"""
def __init__(self,
name,
env,
policy_net,
value_net,
global_counter,
discount_factor=0.99,
summary_writer=None,
max_global_steps=None):
self.name = name
self.discount_factor = discount_factor
self.max_global_steps = max_global_steps
self.global_step = tf.contrib.framework.get_global_step()
self.global_policy_net = policy_net
self.global_value_net = value_net
self.global_counter = global_counter
self.local_counter = itertools.count()
self.summary_writer = summary_writer
self.env = env
# Create local policy/value nets that are not updated asynchronously
with tf.variable_scope(name):
if LSTM_POLICY:
self.policy_net = LSTMPolicyEstimator(policy_net.num_outputs)
else:
self.policy_net = PolicyEstimator(policy_net.num_outputs)
self.value_net = ValueEstimator(reuse=True)
# Op to copy params from global policy/valuenets
self.copy_params_op = make_copy_params_op(
tf.contrib.slim.get_variables(
scope="global", collection=tf.GraphKeys.TRAINABLE_VARIABLES),
tf.contrib.slim.get_variables(
scope=self.name, collection=tf.GraphKeys.TRAINABLE_VARIABLES))
self.vnet_train_op = make_train_op(self.value_net,
self.global_value_net)
self.pnet_train_op = make_train_op(self.policy_net,
self.global_policy_net)
self.state = None
def run(self, sess, coord, t_max):
with sess.as_default(), sess.graph.as_default():
# Initial state
self.state = self.env.reset()
try:
while not coord.should_stop():
# Copy Parameters from the global networks
sess.run(self.copy_params_op)
# Collect some experience
transitions, local_t, global_t = self.run_n_steps(
t_max, sess)
if self.max_global_steps is not None and global_t >= self.max_global_steps:
tf.logging.info(
"Reached global step {}. Stopping.".format(
global_t))
coord.request_stop()
return
# Update the global networks
self.update(transitions, sess)
except tf.errors.CancelledError:
return
def run_n_steps(self, n, sess):
transitions = []
if LSTM_POLICY:
self.policy_net.reset_lstm_features()
for _ in range(n):
# Take a step
if LSTM_POLICY:
action_probs = self.policy_net.action_inference(self.state)
else:
action_probs = self.policy_net.action_prediction(self.state)
# eps-greedy action
action = np.random.choice(np.arange(len(action_probs)),
p=action_probs)
next_state, reward, done, _ = self.env.step(action)
# Store transition
transitions.append(
Transition(state=self.state,
action=action,
reward=reward,
next_state=next_state,
done=done))
# Increase local and global counters
local_t = next(self.local_counter)
global_t = next(self.global_counter)
if local_t % 1000 == 0:
tf.logging.info("{}: local Step {}, global step {}".format(
self.name, local_t, global_t))
if done:
self.state = self.env.reset()
### reset features
if LSTM_POLICY:
self.policy_net.reset_lstm_features()
break
else:
self.state = next_state
return transitions, local_t, global_t
def update(self, transitions, sess):
"""
Updates global policy and value networks based on collected experience
Args:
transitions: A list of experience transitions
sess: A Tensorflow session
"""
# If we episode was not done we bootstrap the value from the last state
reward = 0.0
if not transitions[-1].done:
reward = self.value_net.predict_value(transitions[-1].next_state)
if LSTM_POLICY:
init_lstm_state = self.policy_net.get_init_features()
# Accumulate minibatch exmaples
states = []
policy_targets = []
value_targets = []
actions = []
features = []
for transition in transitions[::-1]:
reward = transition.reward + self.discount_factor * reward
policy_target = (reward -
self.value_net.predict_value(transition.state))
# Accumulate updates
states.append(transition.state)
actions.append(transition.action)
policy_targets.append(policy_target)
value_targets.append(reward)
if LSTM_POLICY:
feed_dict = {
self.policy_net.states: np.array(states),
self.policy_net.targets: policy_targets,
self.policy_net.actions: actions,
self.policy_net.state_in[0]: np.array(init_lstm_state[0]),
self.policy_net.state_in[1]: np.array(init_lstm_state[1]),
self.value_net.states: np.array(states),
self.value_net.targets: value_targets,
}
else:
feed_dict = {
self.policy_net.states: np.array(states),
self.policy_net.targets: policy_targets,
self.policy_net.actions: actions,
self.value_net.states: np.array(states),
self.value_net.targets: value_targets,
}
# Train the global estimators using local gradients
global_step, pnet_loss, vnet_loss, _, _, pnet_summaries, vnet_summaries = sess.run(
[
self.global_step, self.policy_net.loss, self.value_net.loss,
self.pnet_train_op, self.vnet_train_op,
self.policy_net.summaries, self.value_net.summaries
], feed_dict)
# Write summaries
if self.summary_writer is not None and global_step % SUMMARY_EACH_STEPS == 0:
self.summary_writer.add_summary(pnet_summaries, global_step)
self.summary_writer.add_summary(vnet_summaries, global_step)
self.summary_writer.flush()
return pnet_loss, vnet_loss, pnet_summaries, vnet_summaries
