state_shape = env.observation_space.shape
__agent = DQN(
f_create_q=f_net, state_shape=state_shape,
# OneStepTD arguments
num_actions=num_actions, discount_factor=gamma, ddqn=if_ddqn,
# target network sync arguments
target_sync_interval=target_sync_interval,
target_sync_rate=target_sync_rate,
# epsilon greedy arguments
greedy_epsilon=greedy_epsilon,
# optimizer arguments
network_optimizer=LocalOptimizer(optimizer_td, max_grad_norm),
# sampler arguments
sampler=TransitionSampler(
replay_buffer,
batch_size=batch_size,
interval=update_interval,
minimum_count=sample_mimimum_count),
# checkpoint
global_step=global_step
)
# Utilities
stepsSaver = StepsSaver(our_log_dir)
reward_vector2scalar = FuncReward(gamma)
# Configure sess
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = gpu_mem_fraction
with __agent.create_session(
config=config, save_dir=tf_log_dir,
save_checkpoint_secs=save_checkpoint_secs) as sess, \
AsynchronousAgent(
state_shape=state_shape,
# OneStepTD arguments
num_actions=len(ACTIONS),
discount_factor=0.9,
ddqn=False,
# target network sync arguments
target_sync_interval=1,
target_sync_rate=target_sync_rate,
# epsilon greeedy arguments
greedy_epsilon=0.2,
# optimizer arguments
network_optimizer=hrl.network.LocalOptimizer(optimizer_td, 10.0),
# max_gradient=10.0,
# sampler arguments
sampler=TransitionSampler(BalancedMapPlayback(num_actions=len(ACTIONS),
capacity=15000),
batch_size=8,
interval=1),
# checkpoint
global_step=global_step)
def log_info(update_info):
global action_fraction
global action_td_loss
global agent
global next_state
global ACTIONS
global n_steps
global done
global cum_td_loss
global cum_reward
