def build_graph(self):
# Build graph
sg_global_step = graph.GlobalStep()
sg_network = Network()
self.actor = sg_network.actor
self.critic = sg_network.critic
if da3c_config.config.optimizer == 'Adam':
sg_actor_optimizer = optimizer.AdamOptimizer(
da3c_config.config.initial_learning_rate)
sg_critic_optimizer = optimizer.AdamOptimizer(
da3c_config.config.initial_learning_rate)
else:
sg_learning_rate = da3c_graph.LearningRate(
sg_global_step, da3c_config.config.initial_learning_rate)
sg_actor_optimizer = optimizer.RMSPropOptimizer(
learning_rate=sg_learning_rate,
decay=da3c_config.config.RMSProp.decay,
momentum=0.0,
epsilon=da3c_config.config.RMSProp.epsilon)
sg_critic_optimizer = optimizer.RMSPropOptimizer(
learning_rate=sg_learning_rate,
decay=da3c_config.config.RMSProp.decay,
momentum=0.0,
epsilon=da3c_config.config.RMSProp.epsilon)
sg_actor_gradients = optimizer.Gradients(self.actor.weights,
optimizer=sg_actor_optimizer)
sg_critic_gradients = optimizer.Gradients(
self.critic.weights, optimizer=sg_critic_optimizer)
if da3c_config.config.use_icm:
sg_icm_optimizer = optimizer.AdamOptimizer(
da3c_config.config.icm.lr)
sg_icm_weights = icm_model.ICM().weights
sg_icm_gradients = optimizer.Gradients(sg_icm_weights,
optimizer=sg_icm_optimizer)
# Expose ICM public API
self.op_icm_get_weights = self.Op(sg_icm_weights)
self.op_icm_apply_gradients = self.Op(
sg_icm_gradients.apply,
gradients=sg_icm_gradients.ph_gradients)
sg_initialize = graph.Initialize()
# Expose public API
self.op_n_step = self.Op(sg_global_step.n)
self.op_check_weights = self.Ops(self.actor.weights.check,
self.critic.weights.check)
self.op_get_weights = self.Ops(self.actor.weights, self.critic.weights)
self.op_apply_gradients = self.Ops(
sg_actor_gradients.apply,
sg_critic_gradients.apply,
sg_global_step.increment,
gradients=(sg_actor_gradients.ph_gradients,
sg_critic_gradients.ph_gradients),
increment=sg_global_step.ph_increment)
self.op_initialize = self.Op(sg_initialize)
def build_graph(self):
sg_weights = _ManagerNetwork().weights
sg_global_step = graph.GlobalStep()
# self.learning_rate_input = graph.Placeholder(np.float32, shape=(1,), name="manager_lr")
# tf.placeholder(tf.float32, [], name="manager_lr")
sg_learning_rate = fun_graph.LearningRate(sg_global_step)
sg_optimizer = optimizer.RMSPropOptimizer(
learning_rate=sg_learning_rate,
decay=cfg.RMSProp.decay,
momentum=0.0,
epsilon=cfg.RMSProp.epsilon)
sg_gradients = optimizer.Gradients(sg_weights, optimizer=sg_optimizer)
sg_initialize = graph.Initialize()
# Expose public API
self.op_n_step = self.Op(sg_global_step.n)
self.op_get_weights = self.Op(sg_weights)
self.op_apply_gradients = self.Ops(
sg_gradients.apply,
sg_global_step.increment,
gradients=sg_gradients.ph_gradients,
increment=sg_global_step.ph_increment)
self.op_initialize = self.Op(sg_initialize)
def build_graph(self):
sg_global_step = graph.GlobalStep()
sg_network = Network()
sg_get_weights_flatten = graph.GetVariablesFlatten(sg_network.weights)
sg_set_weights_flatten = graph.SetVariablesFlatten(sg_network.weights)
if config.use_linear_schedule:
sg_learning_rate = lr_schedule.Linear(sg_global_step, config)
else:
sg_learning_rate = config.initial_learning_rate
if config.optimizer == 'Adam':
sg_optimizer = optimizer.AdamOptimizer(sg_learning_rate)
elif config.optimizer == 'RMSProp':
sg_optimizer = optimizer.RMSPropOptimizer(
learning_rate=sg_learning_rate,
decay=config.RMSProp.decay,
epsilon=config.RMSProp.epsilon)
else:
assert False, 'There 2 valid options for optimizers: Adam | RMSProp'
sg_gradients_apply = optimizer.Gradients(sg_network.weights,
optimizer=sg_optimizer)
sg_average_reward = graph.LinearMovingAverage(
config.avg_in_num_batches)
sg_initialize = graph.Initialize()
# Expose public API
self.op_n_step = self.Op(sg_global_step.n)
self.op_score = self.Op(sg_average_reward.average)
self.op_get_weights_signed = self.Ops(sg_network.weights,
sg_global_step.n)
self.op_assign_weights = self.Op(sg_network.weights.assign,
weights=sg_network.weights.ph_weights)
self.op_apply_gradients = self.Ops(
sg_gradients_apply.apply,
sg_global_step.increment,
gradients=sg_gradients_apply.ph_gradients,
increment=sg_global_step.ph_increment)
self.op_add_rewards_to_model_score_routine = self.Ops(
sg_average_reward.add,
reward_sum=sg_average_reward.ph_sum,
reward_weight=sg_average_reward.ph_count)
self.op_get_weights_flatten = self.Op(sg_get_weights_flatten)
self.op_set_weights_flatten = self.Op(
sg_set_weights_flatten, value=sg_set_weights_flatten.ph_value)
# Gradient combining routines
self.op_submit_gradients = self.Call(
graph.get_gradients_apply_routine(config))
self.op_initialize = self.Op(sg_initialize)
def build_graph(self):
sg_global_step = graph.GlobalStep()
sg_network = Network()
if config.optimizer == 'Adam':
sg_optimizer = optimizer.AdamOptimizer(
config.initial_learning_rate)
elif config.optimizer == 'RMSProp':
param = {}
if hasattr(config, 'RMSProp'):
if hasattr(config.RMSProp, "decay"):
param["decay"] = config.RMSProp.decay
if hasattr(config.RMSProp, "epsilon"):
param["epsilon"] = config.RMSProp.epsilon
sg_optimizer = optimizer.RMSPropOptimizer(
config.initial_learning_rate, **param)
else:
raise NotImplementedError
sg_gradients_apply = optimizer.Gradients(sg_network.weights,
optimizer=sg_optimizer)
sg_initialize = graph.Initialize()
# Expose public API
self.op_n_step = self.Op(sg_global_step.n)
self.op_get_weights = self.Op(sg_network.weights)
self.op_assign_weights = self.Op(sg_network.weights.assign,
weights=sg_network.weights.ph_weights)
self.op_apply_gradients = self.Ops(
sg_gradients_apply.apply,
sg_global_step.increment,
gradients=sg_gradients_apply.ph_gradients,
n_steps=sg_global_step.ph_increment)
self.op_initialize = self.Op(sg_initialize)
def build_graph(self):
sg_weights = _WorkerNetwork().weights
sg_global_step = graph.GlobalStep()
sg_learning_rate = fun_graph.LearningRate(sg_global_step)
sg_optimizer = optimizer.RMSPropOptimizer(
learning_rate=sg_learning_rate,
decay=cfg.RMSProp.decay,
momentum=0.0,
epsilon=cfg.RMSProp.epsilon)
sg_gradients = optimizer.Gradients(sg_weights, optimizer=sg_optimizer)
sg_initialize = graph.Initialize()
# Expose public API
self.op_n_step = self.Op(sg_global_step.n)
self.op_get_weights = self.Op(sg_weights)
self.op_apply_gradients = self.Ops(
sg_gradients.apply,
sg_global_step.increment,
gradients=sg_gradients.ph_gradients,
increment=sg_global_step.ph_increment)
self.op_initialize = self.Op(sg_initialize)
def build_graph(self):
# Build graph
sg_global_step = graph.GlobalStep()
sg_network = Network()
sg_weights = sg_network.weights
if da3c_config.config.use_linear_schedule:
sg_learning_rate = lr_schedule.Linear(sg_global_step,
da3c_config.config)
else:
sg_learning_rate = da3c_config.config.initial_learning_rate
if da3c_config.config.optimizer == 'Adam':
sg_optimizer = optimizer.AdamOptimizer(sg_learning_rate)
else:
sg_optimizer = optimizer.RMSPropOptimizer(
learning_rate=sg_learning_rate,
decay=da3c_config.config.RMSProp.decay,
momentum=0.0,
epsilon=da3c_config.config.RMSProp.epsilon)
sg_gradients = optimizer.Gradients(sg_weights, optimizer=sg_optimizer)
if da3c_config.config.use_icm:
sg_icm_optimizer = optimizer.AdamOptimizer(
da3c_config.config.icm.lr)
sg_icm_weights = icm_model.ICM().weights
sg_icm_gradients = optimizer.Gradients(sg_icm_weights,
optimizer=sg_icm_optimizer)
# Expose ICM public API
self.op_icm_get_weights = self.Op(sg_icm_weights)
self.op_icm_apply_gradients = self.Op(
sg_icm_gradients.apply,
gradients=sg_icm_gradients.ph_gradients)
sg_average_reward = graph.LinearMovingAverage(
da3c_config.config.avg_in_num_batches)
sg_initialize = graph.Initialize()
# Expose public API
self.op_n_step = self.Op(sg_global_step.n)
self.op_score = self.Op(sg_average_reward.average)
self.op_check_weights = self.Op(sg_weights.check)
self.op_get_weights = self.Ops(sg_weights, sg_global_step.n)
self.op_apply_gradients = self.Ops(
sg_gradients.apply,
sg_global_step.increment,
gradients=sg_gradients.ph_gradients,
increment=sg_global_step.ph_increment)
self.op_add_rewards_to_model_score_routine = self.Ops(
sg_average_reward.add,
reward_sum=sg_average_reward.ph_sum,
reward_weight=sg_average_reward.ph_count)
# Determine Gradients' applying methods: fifo (by default), averaging, delay compensation
sg_get_weights_flatten = graph.GetVariablesFlatten(sg_weights)
sg_set_weights_flatten = graph.SetVariablesFlatten(sg_weights)
self.op_get_weights_flatten = self.Op(sg_get_weights_flatten)
self.op_set_weights_flatten = self.Op(
sg_set_weights_flatten, value=sg_set_weights_flatten.ph_value)
self.op_submit_gradients = self.Call(
graph.get_gradients_apply_routine(da3c_config.config))
self.op_initialize = self.Op(sg_initialize)