def main():
parser = argparse.ArgumentParser()
parser.add_argument('rom', help="File path of the rom")
parser.add_argument('-a', '--agent', help='Agent')
parser.add_argument('-c',
'--agent-config',
help="Agent configuration file")
parser.add_argument('-n',
'--network-config',
help="Network configuration file")
parser.add_argument('-fs',
'--frame-skip',
help="Number of frames to repeat action",
type=int,
default=1)
parser.add_argument('-rap',
'--repeat-action-probability',
help="Repeat action probability",
type=float,
default=0.0)
parser.add_argument('-lolt',
'--loss-of-life-termination',
help="Loss of life counts as terminal state",
action='store_true')
parser.add_argument('-lolr',
'--loss-of-life-reward',
help="Loss of life reward/penalty. EX: -1 to penalize",
type=float,
default=0.0)
parser.add_argument('-ds',
'--display-screen',
action='store_true',
default=False,
help="Display emulator screen")
parser.add_argument('-e',
'--episodes',
type=int,
default=50000,
help="Number of episodes")
parser.add_argument('-t',
'--max-timesteps',
type=int,
default=2000,
help="Maximum number of timesteps per episode")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se',
'--save-episodes',
type=int,
default=100,
help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs")
args = parser.parse_args()
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG) # configurable!!!
logger.addHandler(logging.StreamHandler(sys.stdout))
environment = ALE(args.rom,
frame_skip=args.frame_skip,
repeat_action_probability=args.repeat_action_probability,
loss_of_life_termination=args.loss_of_life_termination,
loss_of_life_reward=args.loss_of_life_reward,
display_screen=args.display_screen)
if args.agent_config:
agent_config = Configuration.from_json(args.agent_config)
else:
agent_config = Configuration()
logger.info("No agent configuration provided.")
if args.network_config:
network = from_json(args.network_config)
else:
network = None
logger.info("No network configuration provided.")
agent_config.default(
dict(states=environment.states,
actions=environment.actions,
network=network))
agent = agents[args.agent](config=agent_config)
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.load_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent_config)
if args.save:
save_dir = os.path.dirname(args.save)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError(
"Cannot save agent to dir {} ()".format(save_dir))
runner = Runner(agent=agent,
environment=environment,
repeat_actions=1,
save_path=args.save,
save_episodes=args.save_episodes)
report_episodes = args.episodes // 1000
if args.debug:
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
sps = r.total_timesteps / (time.time() - r.start_time)
logger.info(
"Finished episode {ep} after {ts} timesteps. Steps Per Second {sps}"
.format(ep=r.episode, ts=r.timestep, sps=sps))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(
sum(r.episode_rewards[-500:]) / 500))
logger.info("Average of last 100 rewards: {}".format(
sum(r.episode_rewards[-100:]) / 100))
return True
logger.info("Starting {agent} for Environment '{env}'".format(
agent=agent, env=environment))
runner.run(args.episodes,
args.max_timesteps,
episode_finished=episode_finished)
logger.info(
"Learning finished. Total episodes: {ep}".format(ep=runner.episode))
environment.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('rom', help="File path of the rom")
parser.add_argument('-a', '--agent', help='Agent')
parser.add_argument('-c',
'--agent-config',
help="Agent configuration file")
parser.add_argument('-n',
'--network-config',
help="Network configuration file")
parser.add_argument(
'-w',
'--workers',
help="Number of threads to run where the model is shared",
type=int,
default=16)
parser.add_argument('-fs',
'--frame-skip',
help="Number of frames to repeat action",
type=int,
default=1)
parser.add_argument('-rap',
'--repeat-action-probability',
help="Repeat action probability",
type=float,
default=0.0)
parser.add_argument('-lolt',
'--loss-of-life-termination',
help="Loss of life counts as terminal state",
action='store_true')
parser.add_argument('-lolr',
'--loss-of-life-reward',
help="Loss of life reward/penalty. EX: -1 to penalize",
type=float,
default=0.0)
parser.add_argument(
'-ea',
'--epsilon-annealing',
help='Create separate epislon annealing schedules per thread',
action='store_true')
parser.add_argument('-ds',
'--display-screen',
action='store_true',
default=False,
help="Display emulator screen")
parser.add_argument('-e',
'--episodes',
type=int,
default=50000,
help="Number of episodes")
parser.add_argument('-t',
'--max-timesteps',
type=int,
default=2000,
help="Maximum number of timesteps per episode")
parser.add_argument('-s', '--save', help="Save agent to this dir")
parser.add_argument('-se',
'--save-episodes',
type=int,
default=100,
help="Save agent every x episodes")
parser.add_argument('-l', '--load', help="Load agent from this dir")
parser.add_argument('-D',
'--debug',
action='store_true',
default=False,
help="Show debug outputs")
args = parser.parse_args()
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG) # configurable!!!
logger.addHandler(logging.StreamHandler(sys.stdout))
environments = [
ALE(args.rom,
frame_skip=args.frame_skip,
repeat_action_probability=args.repeat_action_probability,
loss_of_life_termination=args.loss_of_life_termination,
loss_of_life_reward=args.loss_of_life_reward,
display_screen=args.display_screen) for t in range(args.workers)
]
if args.network_config:
network = from_json(args.network_config)
else:
network = None
logger.info("No network configuration provided.")
agent_configs = []
for i in range(args.workers):
agent_config = Configuration.from_json(args.agent_config)
# optionally overwrite epsilon final values
if "exploration" in agent_config and "epsilon" in agent_config.exploration.type:
# epsilon annealing is based on the global step so divide by the total workers
epsilon_timesteps = agent_config.exploration.epsilon_timesteps // args.workers
agent_config.exploration.epsilon_timesteps = epsilon_timesteps
if args.epsilon_annealing:
# epsilon final values are [0.5, 0.1, 0.01] with probabilities [0.3, 0.4, 0.3]
epsilon_final = np.random.choice([0.5, 0.1, 0.01],
p=[0.3, 0.4, 0.3])
agent_config.exploration.epsilon_final = epsilon_final
agent_config.default(
dict(states=environments[0].states,
actions=environments[0].actions,
network=network))
agent_configs.append(agent_config)
# let the first agent create the model
agent = AgentsDictionary[args.agent](config=agent_configs[-1])
# then create agents with a shared model
agents = [
AgentsDictionary[args.agent](config=agent_configs[t],
model=agent.model)
for t in range(args.workers - 1)
] + [agent]
if args.load:
load_dir = os.path.dirname(args.load)
if not os.path.isdir(load_dir):
raise OSError(
"Could not load agent from {}: No such directory.".format(
load_dir))
agent.load_model(args.load)
if args.debug:
logger.info("-" * 16)
logger.info("Configuration:")
logger.info(agent_config)
if args.save:
save_dir = os.path.dirname(args.save)
if not os.path.isdir(save_dir):
try:
os.mkdir(save_dir, 0o755)
except OSError:
raise OSError(
"Cannot save agent to dir {} ()".format(save_dir))
def episode_finished(stats):
if args.debug:
logger.info(
"Thread {t}. Finished episode {ep} after {ts} timesteps. Reward {r}"
.format(t=stats['thread_id'],
ep=stats['episode'],
ts=stats['timestep'],
r=stats['episode_reward']))
return True
def summary_report(r):
et = time.time()
logger.info('=' * 40)
logger.info('Current Step/Episode: {}/{}'.format(
r.global_step, r.global_episode))
logger.info('SPS: {}'.format(r.global_step / (et - r.start_time)))
reward_list = r.episode_rewards
if len(reward_list) > 0:
logger.info('Max Reward: {}'.format(np.max(reward_list)))
logger.info("Average of last 500 rewards: {}".format(
sum(reward_list[-500:]) / 500))
logger.info("Average of last 100 rewards: {}".format(
sum(reward_list[-100:]) / 100))
logger.info('=' * 40)
# create runners
threaded_runner = ThreadedRunner(agents,
environments,
repeat_actions=1,
save_path=args.save,
save_episodes=args.save_episodes)
logger.info("Starting {agent} for Environment '{env}'".format(
agent=agent, env=environments[0]))
threaded_runner.run(summary_interval=100,
episode_finished=episode_finished,
summary_report=summary_report)
logger.info("Learning finished. Total episodes: {ep}".format(
ep=threaded_runner.global_episode))
[environments[t].close() for t in range(args.workers)]