def main():
gym_id = 'CartPole-v0'
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
max_episodes = 10000
max_timesteps = 1000
env = OpenAIGym(gym_id, monitor=False, monitor_video=False)
config = Configuration(repeat_actions=1,
actions=env.actions,
states=env.states,
exploration='constant',
exploration_args=[0.1],
network=[{
"type": "linear",
"size": 16
}])
agent = create_agent(SimpleQAgent, config)
runner = Runner(agent, env)
def episode_finished(r):
if r.episode % 10 == 0:
logger.info("Finished episode {ep} after {ts} timesteps".format(
ep=r.episode + 1, ts=r.timestep + 1))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 10 rewards: {}".format(
np.mean(r.episode_rewards[-10:])))
return True
logger.info("Starting {agent} for Environment '{env}'".format(agent=agent,
env=env))
runner.run(max_episodes, max_timesteps, episode_finished=episode_finished)
logger.info("Learning finished. Total episodes: {ep}".format(
ep=runner.episode + 1))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="ID of the gym environment")
parser.add_argument('-a', '--agent', default='DQNAgent')
parser.add_argument('-c',
'--agent-config',
help="Agent configuration file")
parser.add_argument('-n',
'--network-config',
help="Network configuration file")
parser.add_argument('-e',
'--episodes',
type=int,
default=50000,
help="Number of episodes")
parser.add_argument('-t',
'--max-timesteps',
type=int,
default=2000 * 60,
help="Maximum number of timesteps per episode")
# parser.add_argument('-m', '--monitor', help="Save results to this directory")
# parser.add_argument('-ms', '--monitor-safe', action='store_true', default=False, help="Do not overwrite previous results")
# parser.add_argument('-mv', '--monitor-video', type=int, default=0, help="Save video every x steps (0 = disabled)")
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()
env = OpenAIUniverse(args.gym_id)
env.configure(remotes=1)
default = dict(repeat_actions=1,
actions=env.actions,
states=env.states,
max_episode_length=args.max_timesteps)
if args.agent_config:
config = Configuration.from_json(args.agent_config)
else:
config = Configuration()
config.default(default)
if args.network_config:
network_config = Configuration.from_json(
args.network_config).network_layers
else:
if config.network_layers:
network_config = config.network_layers
else:
raise TensorForceError("Error: No network configuration provided.")
if args.debug:
print("Configuration:")
print(config)
logger = logging.getLogger(__name__)
logger.setLevel(log_levels[config['loglevel']])
# preprocessing_config = config['preprocessing']
# if preprocessing_config:
# stack = build_preprocessing_stack(preprocessing_config)
# config.states['shape'] = stack.shape(config.states['shape'])
# else:
stack = None
agent = create_agent(args.agent, config, network_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(config)
runner = Runner(agent,
env,
preprocessor=stack,
repeat_actions=config.repeat_actions)
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.save_model(args.save, args.save_episodes)
report_episodes = args.episodes // 1000
if args.debug:
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
logger.info("Finished episode {ep} after {ts} timesteps".format(
ep=r.episode, ts=r.timestep))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(
np.mean(r.episode_rewards[-500:])))
logger.info("Average of last 100 rewards: {}".format(
np.mean(r.episode_rewards[-100:])))
return True
logger.info("Starting {agent} for Environment '{env}'".format(agent=agent,
env=env))
runner.run(args.episodes,
args.max_timesteps,
episode_finished=episode_finished)
logger.info("Learning finished. Total episodes: {ep}".format(
ep=runner.episode + 1))
if args.monitor:
env.gym.monitor.close()
env.close()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('gym_id', help="ID of the gym environment")
parser.add_argument('-a', '--agent', default='DQNAgent')
parser.add_argument('-c', '--agent-config', help="Agent configuration file")
parser.add_argument('-n', '--network-config', help="Network configuration file")
parser.add_argument('-e', '--episodes', type=int, default=50000, help="Number of episodes")
parser.add_argument('-t', '--max-timesteps', type=int, default=2000*60, help="Maximum number of timesteps per episode")
# parser.add_argument('-m', '--monitor', help="Save results to this directory")
# parser.add_argument('-ms', '--monitor-safe', action='store_true', default=False, help="Do not overwrite previous results")
# parser.add_argument('-mv', '--monitor-video', type=int, default=0, help="Save video every x steps (0 = disabled)")
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()
env = OpenAIUniverse(args.gym_id)
env.configure(remotes=1)
default = dict(
repeat_actions=1,
actions=env.actions,
states=env.states,
max_episode_length=args.max_timesteps
)
if args.agent_config:
config = Configuration.from_json(args.agent_config)
else:
config = Configuration()
config.default(default)
if args.network_config:
network_config = Configuration.from_json(args.network_config).network_layers
else:
if config.network_layers:
network_config = config.network_layers
else:
raise TensorForceError("Error: No network configuration provided.")
if args.debug:
print("Configuration:")
print(config)
logger = logging.getLogger(__name__)
logger.setLevel(log_levels[config.loglevel])
stack = None
agent = create_agent(args.agent, config, network_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(config)
runner = Runner(agent, env, preprocessor=stack, repeat_actions=config.repeat_actions)
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.save_model(args.save, args.save_episodes)
report_episodes = args.episodes // 1000
if args.debug:
report_episodes = 1
def episode_finished(r):
if r.episode % report_episodes == 0:
logger.info("Finished episode {ep} after {ts} timesteps".format(ep=r.episode, ts=r.timestep))
logger.info("Episode reward: {}".format(r.episode_rewards[-1]))
logger.info("Average of last 500 rewards: {}".format(np.mean(r.episode_rewards[-500:])))
logger.info("Average of last 100 rewards: {}".format(np.mean(r.episode_rewards[-100:])))
return True
logger.info("Starting {agent} for Environment '{env}'".format(agent=agent, env=env))
runner.run(args.episodes, args.max_timesteps, episode_finished=episode_finished)
logger.info("Learning finished. Total episodes: {ep}".format(ep=runner.episode))
if args.monitor:
env.gym.monitor.close()
env.close()