def main():
print("Creating environment...")
environment = gym_tetris.make('Tetris-v0')
print("Creating model...")
model = modelutils.create_model(number_of_actions)
model.summary()
print("Creating agent...")
if agent_type == "dqn":
agent = DQNAgent(
name="tetris-dqn",
environment=environment,
model=model,
observation_transformation=utils.resize_and_bgr2gray,
observation_frames=4,
number_of_iterations=1000000,
gamma=0.95,
final_epsilon=0.01,
initial_epsilon=1.0,
replay_memory_size=2000,
minibatch_size=32
)
elif agent_type == "ddqn":
agent = DDQNAgent(
name="tetris-ddqn",
environment=environment,
model=model,
observation_transformation=utils.resize_and_bgr2gray,
observation_frames=4,
number_of_iterations=1000000,
gamma=0.95,
final_epsilon=0.01,
initial_epsilon=1.0,
replay_memory_size=2000,
minibatch_size=32,
model_copy_interval=100
)
agent.enable_rewards_tracking(rewards_running_means_length=10000)
agent.enable_episodes_tracking(episodes_running_means_length=100)
agent.enable_maxq_tracking(maxq_running_means_length=10000)
agent.enable_model_saving(model_save_frequency=10000)
agent.enable_plots_saving(plots_save_frequency=10000)
print("Training ...")
agent.fit(verbose=True, headless="headless" in sys.argv, render_states=True)
def main():
print("Creating model...")
model = create_model()
model.summary()
print("Creating environment...")
environment = gym.make("CartPole-v0")
environment._max_episode_steps = 500
print("Creating agent...")
if agent_type == "dqn":
agent = DQNAgent(name="cartpole-dqn",
model=model,
environment=environment,
observation_frames=1,
observation_transformation=observation_transformation,
reward_transformation=reward_transformation,
gamma=0.95,
final_epsilon=0.01,
initial_epsilon=1.0,
number_of_iterations=1000000,
replay_memory_size=2000,
minibatch_size=32)
elif agent_type == "ddqn":
agent = DDQNAgent(
name="cartpole-ddqn",
model=model,
environment=environment,
observation_frames=1,
observation_transformation=observation_transformation,
reward_transformation=reward_transformation,
gamma=0.95,
final_epsilon=0.01,
initial_epsilon=1.0,
number_of_iterations=1000000,
replay_memory_size=2000,
minibatch_size=32,
model_copy_interval=100)
agent.enable_rewards_tracking(rewards_running_means_length=10000)
agent.enable_episodes_tracking(episodes_running_means_length=10000)
agent.enable_maxq_tracking(maxq_running_means_length=10000)
agent.enable_model_saving(model_save_frequency=100000)
agent.enable_tensorboard_for_tracking()
print("Training ...")
agent.fit(verbose=True, headless="render" not in sys.argv)
def main():
print("Creating model...")
model = modelutils.create_model(number_of_actions=4)
model.summary()
print("Creating agent...")
if agent_type == "dqn":
agent = DQNAgent(name="doom-dqn",
model=model,
number_of_actions=4,
gamma=0.99,
final_epsilon=0.0001,
initial_epsilon=0.1,
number_of_iterations=200000,
replay_memory_size=10000,
minibatch_size=32)
elif agent_type == "ddqn":
agent = DDQNAgent(name="doom-ddqn",
model=model,
number_of_actions=4,
gamma=0.99,
final_epsilon=0.0001,
initial_epsilon=0.1,
number_of_iterations=200000,
replay_memory_size=10000,
minibatch_size=32,
model_copy_interval=100)
agent.enable_rewards_tracking(rewards_running_means_length=1000)
agent.enable_episodes_tracking(episodes_running_means_length=1000)
agent.enable_maxq_tracking(maxq_running_means_length=1000)
agent.enable_model_saving(model_save_frequency=10000)
agent.enable_plots_saving(plots_save_frequency=10000)
print("Creating game...")
#environment = Environment(headless=("headless" in sys.argv))
# Create an instance of the Doom game.
environment = DoomGame()
environment.load_config("scenarios/basic.cfg")
environment.set_screen_format(ScreenFormat.GRAY8)
environment.set_window_visible("headless" not in sys.argv)
environment.init()
print("Training ...")
train(agent, environment, verbose="verbose" in sys.argv)
def main():
print("Creating model...")
model = modelutils.create_model(number_of_actions)
model.summary()
print("Creating agent...")
if agent_type == "dqn":
agent = DQNAgent(name="supermario-dqn",
model=model,
number_of_actions=number_of_actions,
gamma=0.95,
final_epsilon=0.01,
initial_epsilon=1.0,
number_of_iterations=1000000,
replay_memory_size=2000,
minibatch_size=32)
elif agent_type == "ddqn":
agent = DDQNAgent(name="supermario-ddqn",
model=model,
number_of_actions=number_of_actions,
gamma=0.95,
final_epsilon=0.01,
initial_epsilon=1.0,
number_of_iterations=1000000,
replay_memory_size=2000,
minibatch_size=32,
model_copy_interval=100)
agent.enable_rewards_tracking(rewards_running_means_length=10000)
agent.enable_episodes_tracking(episodes_running_means_length=100)
agent.enable_maxq_tracking(maxq_running_means_length=10000)
agent.enable_model_saving(model_save_frequency=10000)
agent.enable_plots_saving(plots_save_frequency=10000)
print("Creating game...")
environment = gym_super_mario_bros.make("SuperMarioBros-v0")
environment = BinarySpaceToDiscreteSpaceEnv(environment, actions)
print("Training ...")
train(agent,
environment,
verbose="verbose" in sys.argv,
headless="headless" in sys.argv)