def eval_ale_environment(model_path, render, num_of_epochs, steps_per_epoch,
initial_epsilon, log_dir):
# Create an ALE for game Breakout
environment = ALEEnvironment(ALEEnvironment.RIVERRAID,
is_render=render,
max_episode_steps=5000)
# Create a network configuration for Atari DQN
network_config = PrioritizedAtariDQNConfig(environment)
# Create a policy network for DQN agent
network = PolicyNetwork(network_config, load_model_path=model_path)
# Create DQN agent
agent = DQNAgent(network,
environment,
initial_epsilon=initial_epsilon,
report_frequency=1,
num_of_threads=8,
num_of_epochs=num_of_epochs,
steps_per_epoch=steps_per_epoch,
log_dir=log_dir,
prioritized=True)
# Evaluate it
return agent.evaluate()
def train_ale_environment():
# Create an ALE for game Breakout
environment = ALEEnvironment(ALEEnvironment.BREAKOUT)
###################################################################################
# In case using Gym
# environment = GymEnvironment("Breakout-v0", state_processor=AtariProcessor())
###################################################################################
# Create a network configuration for Atari DQN
network_config = PrioritizedAtariDQNConfig(environment,
initial_beta=0.4,
initial_learning_rate=0.00025,
debug_mode=True)
# Create a policy network for DQN agent
network = PolicyNetwork(network_config, max_num_of_checkpoints=100)
# Create DQN agent
agent = DQNAgent(network,
environment,
save_frequency=5e5,
steps_per_epoch=1e6,
num_of_epochs=50,
exp_replay_size=2**19,
importance_sampling=True,
log_dir="./train/breakout/pdqn_check_point",
prioritized_alpha=0.6,
prioritized=True)
# Train it
agent.train()
def train_ale_environment():
# Create an ALE for game Breakout
environment = ALEEnvironment(ALEEnvironment.BREAKOUT)
# Create a network configuration for Atari DQN
network_config = AtariDQNConfig(environment)
# Put the configuration into a policy network
network = PolicyNetwork(network_config)
# Create a DQN agent
agent = DQNAgent(network,
environment,
log_dir="./train/breakout/dqn_breakout")
# Train it
agent.train()
def train_ale_environment():
# Create an ALE for game Breakout
environment = ALEEnvironment(ALEEnvironment.PONG)
###################################################################################
# In case using Gym
# environment = GymEnvironment("Breakout-v0", state_processor=AtariProcessor())
###################################################################################
# Create a network configuration for Atari DQN using Duel network
network_config = AtariDuelDQNConfig(environment)
# Create a policy network for DQN agent (create maximum of 40 checkpoints)
network = PolicyNetwork(network_config, num_of_checkpoints=40)
# Create DQN agent (Save checkpoint every 30 minutes, stop training at checkpoint 40th)
agent = DQNAgent(network,
environment,
save_time_based=30,
checkpoint_stop=40,
log_dir="./train/Pong/dueldqn_pong_time_based_30_40")
# Train it
agent.train()