def simulation(clients, passed_config):
"""
Performs a simulation of Pig Chase game based on passed config
:param clients: not used, just to have uniform interface
:param passed_config: type str, the name of the config to use
"""
sim_config = Config(passed_config)
experiment_cfg = sim_config.get_section('EXPERIMENT')
results_dir = os.path.join(get_results_path(),
'simulation_{}_{}_{}'.format(sim_config.get_str('BASIC', 'learner'),
sim_config.get_str('BASIC', 'network'),
datetime.utcnow().isoformat()[:-4]))
experiment_cfg["outdir"] = results_dir
sim_config.copy_config(results_dir)
opponent = PigChaseChallengeAgent(name="Agent_1", p_focused=0.95)
agent_env = getattr(env_simulator, sim_config.get_str('BASIC', 'simulator'))(opponent=opponent,
**sim_config.get_section(
'SIMULATOR'))
learner = create_value_based_learner(passed_config)
logger.log(msg='Experiment parameters {}'.format(
' '.join([name + ':' + str(value) for name, value in experiment_cfg.items()])),
level=logging.INFO)
logger.log(msg='Starting experiment, calling chainerrl function.',
level=logging.INFO)
experiments.train_agent_with_evaluation(agent=learner,
env=agent_env,
**experiment_cfg)
def _test_abc(self,
steps=100000,
require_success=True,
gpu=-1,
load_model=False):
if self.recurrent and gpu >= 0:
self.skipTest(
'NStepLSTM does not support double backprop with GPU.')
if self.recurrent and chainer.__version__ == '7.0.0b3':
self.skipTest(
'chainer==7.0.0b3 has a bug in double backrop of LSTM.'
' See https://github.com/chainer/chainer/pull/8037')
env, _ = self.make_env_and_successful_return(test=False)
test_env, successful_return = self.make_env_and_successful_return(
test=True)
agent = self.make_agent(env, gpu)
if load_model:
print('Load agent from', self.agent_dirname)
agent.load(self.agent_dirname)
max_episode_len = None if self.episodic else 2
# Train
train_agent_with_evaluation(
agent=agent,
env=env,
eval_env=test_env,
steps=steps,
outdir=self.tmpdir,
eval_interval=200,
eval_n_steps=None,
eval_n_episodes=5,
successful_score=successful_return,
train_max_episode_len=max_episode_len,
)
agent.stop_episode()
# Test
n_test_runs = 10
eval_returns = run_evaluation_episodes(
test_env,
agent,
n_steps=None,
n_episodes=n_test_runs,
max_episode_len=max_episode_len,
)
if require_success:
n_succeeded = np.sum(np.asarray(eval_returns) >= successful_return)
self.assertEqual(n_succeeded, n_test_runs)
# Save
agent.save(self.agent_dirname)
def _test_abc(self,
steps=1000000,
require_success=True,
gpu=-1,
load_model=False):
env, _ = self.make_env_and_successful_return(test=False)
test_env, successful_return = self.make_env_and_successful_return(
test=True)
agent = self.make_agent(env, gpu)
if load_model:
print('Load agent from', self.agent_dirname)
agent.load(self.agent_dirname)
max_episode_len = None if self.episodic else 2
# Train
train_agent_with_evaluation(
agent=agent,
env=env,
eval_env=test_env,
steps=steps,
outdir=self.tmpdir,
eval_interval=200,
eval_n_steps=None,
eval_n_episodes=5,
successful_score=successful_return,
train_max_episode_len=max_episode_len,
)
agent.stop_episode()
# Test
n_test_runs = 5
for _ in range(n_test_runs):
total_r = 0.0
obs = test_env.reset()
done = False
reward = 0.0
while not done:
action = agent.act(obs)
obs, reward, done, _ = test_env.step(action)
total_r += reward
agent.stop_episode()
if require_success:
self.assertAlmostEqual(total_r, successful_return)
# Save
agent.save(self.agent_dirname)
def _test_training(self,
gpu,
steps=5000,
load_model=False,
require_success=True):
random_seed.set_random_seed(1)
logging.basicConfig(level=logging.DEBUG)
env = self.make_env_and_successful_return(test=False)[0]
test_env, successful_return = self.make_env_and_successful_return(
test=True)
agent = self.make_agent(env, gpu)
if load_model:
print('Load agent from', self.agent_dirname)
agent.load(self.agent_dirname)
agent.replay_buffer.load(self.rbuf_filename)
# Train
train_agent_with_evaluation(agent=agent,
env=env,
steps=steps,
outdir=self.tmpdir,
eval_interval=200,
eval_n_steps=None,
eval_n_episodes=5,
successful_score=1,
eval_env=test_env)
agent.stop_episode()
# Test
n_test_runs = 5
for _ in range(n_test_runs):
total_r = 0.0
obs = test_env.reset()
done = False
reward = 0.0
while not done:
action = agent.act(obs)
obs, reward, done, _ = test_env.step(action)
total_r += reward
agent.stop_episode()
if require_success:
self.assertAlmostEqual(total_r, successful_return)
# Save
agent.save(self.agent_dirname)
agent.replay_buffer.save(self.rbuf_filename)
def _test_abc(self,
steps=100000,
require_success=True,
gpu=-1,
load_model=False):
env, _ = self.make_env_and_successful_return(test=False)
test_env, successful_return = self.make_env_and_successful_return(
test=True)
agent = self.make_agent(env, gpu)
if load_model:
print('Load agent from', self.agent_dirname)
agent.load(self.agent_dirname)
max_episode_len = None if self.episodic else 2
# Train
train_agent_with_evaluation(
agent=agent,
env=env,
eval_env=test_env,
steps=steps,
outdir=self.tmpdir,
eval_interval=200,
eval_n_steps=None,
eval_n_episodes=5,
successful_score=successful_return,
train_max_episode_len=max_episode_len,
)
agent.stop_episode()
# Test
n_test_runs = 5
eval_returns = run_evaluation_episodes(
test_env,
agent,
n_steps=None,
n_episodes=n_test_runs,
max_episode_len=max_episode_len,
)
if require_success:
n_succeeded = np.sum(np.asarray(eval_returns) >= successful_return)
self.assertEqual(n_succeeded, n_test_runs)
# Save
agent.save(self.agent_dirname)
def _test_abc(self,
steps=1000000,
require_success=True,
gpu=-1,
load_model=False):
env, _ = self.make_env_and_successful_return(test=False)
test_env, successful_return = self.make_env_and_successful_return(
test=True)
agent = self.make_agent(env, gpu)
if load_model:
print('Load agent from', self.agent_dirname)
agent.load(self.agent_dirname)
# Train
train_agent_with_evaluation(agent=agent,
env=env,
steps=steps,
outdir=self.tmpdir,
eval_interval=200,
eval_n_runs=50,
successful_score=1,
eval_env=test_env)
agent.stop_episode()
# Test
n_test_runs = 100
n_succeeded = 0
for _ in range(n_test_runs):
total_r = 0.0
obs = test_env.reset()
done = False
reward = 0.0
while not done:
action = agent.act(obs)
obs, reward, done, _ = test_env.step(action)
total_r += reward
agent.stop_episode()
if np.isclose(total_r, successful_return):
n_succeeded += 1
if require_success:
self.assertGreater(n_succeeded, 0.8 * n_test_runs)
# Save
agent.save(self.agent_dirname)
def train(self, episodes):
"""
Trains the model for given number of episodes.
"""
progress_bar = ProgressBar(self.pbar, episodes)
experiments.train_agent_with_evaluation(
self.agent,
self.env,
steps=episodes, # Train the agent for 2000 steps
eval_n_steps=None, # We evaluate for episodes, not time
eval_n_episodes=10, # 10 episodes are sampled for each evaluation
train_max_episode_len=100, # Maximum length of each episode
eval_interval=self.
log_interval, # Evaluate the agent after every 1000 steps
step_hooks=[progress_bar], # add hooks
logger=self.logger,
outdir=self.save_path) # Save everything to 'supervisor' directory
def value_based_experiment(clients, passed_config):
rvb_config = Config(os.path.join(get_config_dir(), passed_config))
results_dir = os.path.join(get_results_path(),
'simulation_{}_{}_{}'.format(rvb_config.get_str('BASIC', 'learner'),
rvb_config.get_str('BASIC', 'network'),
datetime.utcnow().isoformat()[:-6]))
experiment_cfg = rvb_config.get_section('EXPERIMENT')
experiment_cfg["outdir"] = results_dir
opponent = PigChaseChallengeAgent(name="Agent_1")
agent_st_build = CustomStateBuilder()
opponent_st_build = PigChaseSymbolicStateBuilder()
opponent_env = PigChaseEnvironment(remotes=clients,
state_builder=opponent_st_build,
role=0,
randomize_positions=True)
agent_env = PigChaseEnvironment(remotes=clients,
state_builder=agent_st_build,
role=1,
randomize_positions=True)
env = SingleEnvWrapper(agent_env=agent_env,
opponent_env=opponent_env,
opponent=opponent,
reward_norm=ENV_CAUGHT_REWARD)
learner = create_value_based_learner(passed_config)
logger.log(msg='Experiment parameters {}'.format(
' '.join(['{}:{}'.format(name, str(value)) for name, value in experiment_cfg.items()])),
level=logging.INFO)
logger.log(msg='Starting experiment, calling chainerrl function.',
level=logging.INFO)
experiments.train_agent_with_evaluation(agent=learner,
env=env,
**experiment_cfg)
replay_start_size=replay_start_size,
target_update_interval=target_update_interval,
update_interval=update_interval,
phi=phi,
target_update_method=target_update_method,
soft_update_tau=soft_update_tau,
episodic_update_len=16
)
# Start tensorboard writer
writer = SummaryWriter('runs/'+datetime.now().strftime('%B%d %H:%M:%S'))
# Start training
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
eval_env=env,
steps=steps,
eval_n_runs=eval_n_runs,
eval_interval=eval_interval,
outdir=outdir,
max_episode_len=timestep_limit
)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros_like(obs_space.low, dtype=np.float32)[None])],
os.path.join(outdir, 'model')
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env',
type=str,
default='BreakoutNoFrameskip-v4',
help='OpenAI Atari domain to perform algorithm on.')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu',
type=int,
default=0,
help='GPU to use, set to -1 if no GPU.')
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--final-exploration-frames',
type=int,
default=10**6,
help='Timesteps after which we stop ' +
'annealing exploration rate')
parser.add_argument('--final-epsilon',
type=float,
default=0.1,
help='Final value of epsilon during training.')
parser.add_argument('--eval-epsilon',
type=float,
default=0.05,
help='Exploration epsilon used during eval episodes.')
parser.add_argument('--noisy-net-sigma', type=float, default=None)
parser.add_argument('--arch',
type=str,
default='doubledqn',
choices=['nature', 'nips', 'dueling', 'doubledqn'],
help='Network architecture to use.')
parser.add_argument('--steps',
type=int,
default=5 * 10**7,
help='Total number of timesteps to train the agent.')
parser.add_argument(
'--max-frames',
type=int,
default=30 * 60 * 60, # 30 minutes with 60 fps
help='Maximum number of frames for each episode.')
parser.add_argument('--replay-start-size',
type=int,
default=5 * 10**4,
help='Minimum replay buffer size before ' +
'performing gradient updates.')
parser.add_argument('--target-update-interval',
type=int,
default=1 * 10**4,
help='Frequency (in timesteps) at which ' +
'the target network is updated.')
parser.add_argument('--eval-interval',
type=int,
default=10**5,
help='Frequency (in timesteps) of evaluation phase.')
parser.add_argument('--update-interval',
type=int,
default=4,
help='Frequency (in timesteps) of network updates.')
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--no-clip-delta',
dest='clip_delta',
action='store_false')
parser.set_defaults(clip_delta=True)
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--render',
action='store_true',
default=False,
help='Render env states in a GUI window.')
parser.add_argument('--monitor',
action='store_true',
default=False,
help='Monitor env. Videos and additional information'
' are saved as output files.')
parser.add_argument('--lr',
type=float,
default=2.5e-4,
help='Learning rate.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for train and test envs.
train_seed = args.seed
test_seed = 2**31 - 1 - args.seed
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, args.eval_epsilon)
if args.monitor:
env = gym.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
n_actions = env.action_space.n
q_func = links.Sequence(links.NatureDQNHead(), L.Linear(512, n_actions),
DiscreteActionValue)
if args.noisy_net_sigma is not None:
links.to_factorized_noisy(q_func)
# Turn off explorer
explorer = explorers.Greedy()
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyper parameters as the Nature paper's
opt = optimizers.RMSpropGraves(lr=args.lr,
alpha=0.95,
momentum=0.0,
eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
Agent = agents.DQN
agent = Agent(q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
clip_delta=args.clip_delta,
update_interval=args.update_interval,
batch_accumulator='sum',
phi=phi)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(env=eval_env,
agent=agent,
n_runs=args.eval_n_runs)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
save_best_so_far_agent=False,
eval_env=eval_env,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames', type=int, default=10**6)
parser.add_argument('--final-epsilon', type=float, default=0.1)
parser.add_argument('--eval-epsilon', type=float, default=0.05)
parser.add_argument('--arch',
type=str,
default='nature',
choices=['nature', 'nips', 'dueling'])
parser.add_argument('--steps', type=int, default=10**7)
parser.add_argument(
'--max-episode-len',
type=int,
default=5 * 60 * 60 // 4, # 5 minutes with 60/4 fps
help='Maximum number of steps for each episode.')
parser.add_argument('--replay-start-size', type=int, default=5 * 10**4)
parser.add_argument('--target-update-interval', type=int, default=10**4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--activation', type=str, default='relu')
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--no-clip-delta',
dest='clip_delta',
action='store_false')
parser.set_defaults(clip_delta=True)
parser.add_argument('--agent',
type=str,
default='DQN',
choices=['DQN', 'DoubleDQN', 'PAL'])
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--render',
action='store_true',
default=False,
help='Render env states in a GUI window.')
parser.add_argument('--monitor',
action='store_true',
default=False,
help='Monitor env. Videos and additional information'
' are saved as output files.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for train and test envs.
train_seed = args.seed
test_seed = 2**31 - 1 - args.seed
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(args.env),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if args.monitor:
env = gym.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
misc.env_modifiers.make_rendered(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
n_actions = env.action_space.n
activation = parse_activation(args.activation)
q_func = parse_arch(args.arch, n_actions, activation)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyper parameters as the Nature paper's
opt = optimizers.RMSpropGraves(lr=2.5e-4,
alpha=0.95,
momentum=0.0,
eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
Agent = parse_agent(args.agent)
agent = Agent(q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
clip_delta=args.clip_delta,
update_interval=args.update_interval,
batch_accumulator='sum',
phi=phi)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(env=eval_env,
agent=agent,
n_runs=args.eval_n_runs)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
# In testing DQN, randomly select 5% of actions
eval_explorer = explorers.ConstantEpsilonGreedy(
args.eval_epsilon, lambda: np.random.randint(n_actions))
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_explorer=eval_explorer,
save_best_so_far_agent=False,
max_episode_len=args.max_episode_len,
eval_env=eval_env,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames', type=int, default=10**6)
parser.add_argument('--final-epsilon', type=float, default=0.1)
parser.add_argument('--eval-epsilon', type=float, default=0.05)
parser.add_argument('--steps', type=int, default=10**7)
parser.add_argument(
'--max-frames',
type=int,
default=30 * 60 * 60, # 30 minutes with 60 fps
help='Maximum number of frames for each episode.')
parser.add_argument('--replay-start-size', type=int, default=5 * 10**4)
parser.add_argument('--target-update-interval', type=int, default=10**4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--batch-size', type=int, default=32)
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--render',
action='store_true',
default=False,
help='Render env states in a GUI window.')
parser.add_argument('--monitor',
action='store_true',
default=False,
help='Monitor env. Videos and additional information'
' are saved as output files.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for train and test envs.
train_seed = args.seed
test_seed = 2**31 - 1 - args.seed
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, args.eval_epsilon)
if args.monitor:
env = gym.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
n_actions = env.action_space.n
n_atoms = 51
v_max = 10
v_min = -10
q_func = chainerrl.links.Sequence(
chainerrl.links.NatureDQNHead(),
chainerrl.q_functions.DistributionalFCStateQFunctionWithDiscreteAction(
None,
n_actions,
n_atoms,
v_min,
v_max,
n_hidden_channels=0,
n_hidden_layers=0),
)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyper parameters as https://arxiv.org/abs/1707.06887
opt = chainer.optimizers.Adam(2.5e-4, eps=1e-2 / args.batch_size)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = chainerrl.agents.CategoricalDQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
batch_accumulator='mean',
phi=phi,
)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(env=eval_env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
save_best_so_far_agent=False,
eval_env=eval_env,
)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('--outdir', type=str, default='dqn_out')
parser.add_argument('--env', type=str, default='Pendulum-v0')
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--final-exploration-steps', type=int, default=10**4)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=10**5)
parser.add_argument('--prioritized-replay', action='store_true')
parser.add_argument('--episodic-replay', action='store_true')
parser.add_argument('--replay-start-size', type=int, default=1000)
parser.add_argument('--target-update-interval', type=int, default=10**2)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--eval-interval', type=int, default=10**4)
parser.add_argument('--n-hidden-channels', type=int, default=100)
parser.add_argument('--n-hidden-layers', type=int, default=2)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--minibatch-size', type=int, default=None)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1e-3)
args = parser.parse_args()
args.outdir = experiments.prepare_output_dir(args,
args.outdir,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
if args.seed is not None:
misc.set_random_seed(args.seed)
def clip_action_filter(a):
return np.clip(a, action_space.low, action_space.high)
def make_env(for_eval):
env = gym.make(args.env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if isinstance(env.action_space, spaces.Box):
misc.env_modifiers.make_action_filtered(env, clip_action_filter)
if not for_eval:
misc.env_modifiers.make_reward_filtered(
env, lambda x: x * args.reward_scale_factor)
if ((args.render_eval and for_eval)
or (args.render_train and not for_eval)):
misc.env_modifiers.make_rendered(env)
return env
env = make_env(for_eval=False)
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = env.observation_space
obs_size = obs_space.low.size
action_space = env.action_space
if isinstance(action_space, spaces.Box):
action_size = action_space.low.size
# Use NAF to apply DQN to continuous action spaces
q_func = q_functions.FCQuadraticStateQFunction(
obs_size,
action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
action_space=action_space)
# Use the Ornstein-Uhlenbeck process for exploration
ou_sigma = (action_space.high - action_space.low) * 0.2
explorer = explorers.AdditiveOU(sigma=ou_sigma)
else:
n_actions = action_space.n
q_func = q_functions.FCStateQFunctionWithDiscreteAction(
obs_size,
n_actions,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(
args.start_epsilon, args.end_epsilon, args.final_exploration_steps,
action_space.sample)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros_like(obs_space.low, dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
opt = optimizers.Adam()
opt.setup(q_func)
rbuf_capacity = 5 * 10**5
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(rbuf_capacity,
betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
def phi(obs):
return obs.astype(np.float32)
agent = DQN(q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
phi=phi,
minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay,
episodic_update_len=16)
if args.load:
agent.load(args.load)
eval_env = make_env(for_eval=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
max_episode_len=timestep_limit)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env',
type=str,
default='BreakoutNoFrameskip-v4',
help='OpenAI Atari domain to perform algorithm on.')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu',
type=int,
default=0,
help='GPU to use, set to -1 if no GPU.')
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--render',
action='store_true',
default=False,
help='Render env states in a GUI window.')
parser.add_argument('--monitor',
action='store_true',
default=False,
help='Monitor env. Videos and additional information'
' are saved as output files.')
parser.add_argument('--steps',
type=int,
default=5 * 10**7,
help='Total number of timesteps to train the agent.')
parser.add_argument('--replay-start-size',
type=int,
default=5 * 10**4,
help='Minimum replay buffer size before ' +
'performing gradient updates.')
parser.add_argument('--eval-n-steps', type=int, default=125000)
parser.add_argument('--eval-interval', type=int, default=250000)
parser.add_argument('--n-best-episodes', type=int, default=30)
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for train and test envs.
train_seed = args.seed
test_seed = 2**31 - 1 - args.seed
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=None),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, 0.05)
if args.monitor:
env = chainerrl.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
n_actions = env.action_space.n
q_func = links.Sequence(links.NatureDQNHead(), L.Linear(512, n_actions),
DiscreteActionValue)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyperparameters as the Nature paper
opt = optimizers.RMSpropGraves(lr=2.5e-4,
alpha=0.95,
momentum=0.0,
eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
start_epsilon=1.0,
end_epsilon=0.1,
decay_steps=10**6,
random_action_func=lambda: np.random.randint(n_actions))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
Agent = agents.DQN
agent = Agent(q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=10**4,
clip_delta=True,
update_interval=4,
batch_accumulator='sum',
phi=phi)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(env=eval_env,
agent=agent,
n_steps=args.eval_n_steps,
n_episodes=None)
print('n_episodes: {} mean: {} median: {} stdev {}'.format(
eval_stats['episodes'], eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_steps=args.eval_n_steps,
eval_n_episodes=None,
eval_interval=args.eval_interval,
outdir=args.outdir,
save_best_so_far_agent=True,
eval_env=eval_env,
)
dir_of_best_network = os.path.join(args.outdir, "best")
agent.load(dir_of_best_network)
# run 30 evaluation episodes, each capped at 5 mins of play
stats = experiments.evaluator.eval_performance(
env=eval_env,
agent=agent,
n_steps=None,
n_episodes=args.n_best_episodes,
max_episode_len=4500,
logger=None)
with open(os.path.join(args.outdir, 'bestscores.json'), 'w') as f:
json.dump(stats, f)
print("The results of the best scoring network:")
for stat in stats:
print(str(stat) + ":" + str(stats[stat]))
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--env', type=str, default='CartPole-v1')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--final-exploration-steps', type=int, default=1000)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=10**8)
parser.add_argument('--replay-start-size', type=int, default=50)
parser.add_argument('--target-update-interval', type=int, default=100)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--eval-interval', type=int, default=1000)
parser.add_argument('--n-hidden-channels', type=int, default=12)
parser.add_argument('--n-hidden-layers', type=int, default=3)
parser.add_argument('--gamma', type=float, default=0.95)
parser.add_argument('--minibatch-size', type=int, default=32)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1.0)
args = parser.parse_args()
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
args.outdir = experiments.prepare_output_dir(args,
args.outdir,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
env = gym.make(args.env)
env_seed = 2**32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if not test:
misc.env_modifiers.make_reward_filtered(
env, lambda x: x * args.reward_scale_factor)
if ((args.render_eval and test) or (args.render_train and not test)):
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_size = env.observation_space.low.size
action_space = env.action_space
hidden_size = 64
q_func = chainerrl.agents.iqn.ImplicitQuantileQFunction(
psi=chainerrl.links.Sequence(
L.Linear(obs_size, hidden_size),
F.relu,
),
phi=chainerrl.links.Sequence(
chainerrl.agents.iqn.CosineBasisLinear(64, hidden_size),
F.relu,
),
f=L.Linear(hidden_size, env.action_space.n),
)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(args.start_epsilon,
args.end_epsilon,
args.final_exploration_steps,
action_space.sample)
opt = optimizers.Adam(1e-3)
opt.setup(q_func)
rbuf_capacity = 50000 # 5 * 10 ** 5
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
agent = chainerrl.agents.IQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
minibatch_size=args.minibatch_size,
)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
max_episode_len=timestep_limit)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('algo', default='ppo', choices=['ppo', 'gail', 'airl'], type=str)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--env', type=str, default='Hopper-v2')
parser.add_argument('--arch', type=str, default='FFGaussian',
choices=('FFSoftmax', 'FFMellowmax',
'FFGaussian'))
parser.add_argument('--bound-mean', action='store_true')
parser.add_argument('--seed', type=int, default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir', type=str, default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps', type=int, default=10 ** 6)
parser.add_argument('--eval-interval', type=int, default=10000)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--load_demo', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--update-interval', type=int, default=2048)
parser.add_argument('--batchsize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--entropy-coef', type=float, default=0.0)
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu,))
if not (args.demo and args.load):
args.outdir = experiments.prepare_output_dir(args, args.outdir)
def make_env(test):
env = gym.make(args.env)
# Use different random seeds for train and test envs
env_seed = 2 ** 32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if not test:
# Scale rewards (and thus returns) to a reasonable range so that
# training is easier
env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
if args.render:
env = chainerrl.wrappers.Render(env)
return env
sample_env = gym.make(args.env)
timestep_limit = sample_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = sample_env.observation_space
action_space = sample_env.action_space
# Normalize observations based on their empirical mean and variance
obs_normalizer = chainerrl.links.EmpiricalNormalization(
obs_space.low.size, clip_threshold=5)
# Switch policy types accordingly to action space types
if args.arch == 'FFSoftmax':
model = A3CFFSoftmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFMellowmax':
model = A3CFFMellowmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFGaussian':
model = A3CFFGaussian(obs_space.low.size, action_space,
bound_mean=args.bound_mean)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
if args.algo == 'ppo':
agent = PPO(model, opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize, epochs=args.epochs,
clip_eps_vf=None, entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
elif args.algo == 'gail':
import numpy as np
from irl.gail import GAIL
from irl.gail import Discriminator
demonstrations = np.load(args.load_demo)
D = Discriminator(gpu=args.gpu)
agent = GAIL(demonstrations=demonstrations, discriminator=D,
model=model, optimizer=opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize, epochs=args.epochs,
clip_eps_vf=None, entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,)
elif args.algo == 'airl':
import numpy as np
from irl.airl import AIRL as Agent
from irl.airl import Discriminator
# obs_normalizer = None
demonstrations = np.load(args.load_demo)
D = Discriminator(gpu=args.gpu)
agent = Agent(demonstrations=demonstrations, discriminator=D,
model=model, optimizer=opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize, epochs=args.epochs,
clip_eps_vf=None, entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,)
if args.load:
agent.load(args.load)
if args.demo:
env = make_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
outdir = args.load if args.load else args.outdir
save_agent_demo(make_env(False), agent, outdir)
else:
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = max(value, 1e-8)
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.2, 0, clip_eps_setter)
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(False),
eval_env=make_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
train_max_episode_len=timestep_limit,
save_best_so_far_agent=False,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
)
save_agent_demo(make_env(False), agent, args.outdir)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--env', type=str, default='CartPole-v1')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--final-exploration-steps', type=int, default=1000)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=10**8)
parser.add_argument('--prioritized-replay', action='store_true')
parser.add_argument('--episodic-replay', action='store_true')
parser.add_argument('--replay-start-size', type=int, default=50)
parser.add_argument('--target-update-interval', type=int, default=100)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--eval-interval', type=int, default=1000)
parser.add_argument('--n-hidden-channels', type=int, default=12)
parser.add_argument('--n-hidden-layers', type=int, default=3)
parser.add_argument('--gamma', type=float, default=0.95)
parser.add_argument('--minibatch-size', type=int, default=None)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1.0)
args = parser.parse_args()
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
args.outdir = experiments.prepare_output_dir(args,
args.outdir,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
env = gym.make(args.env)
env_seed = 2**32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if not test:
# Scale rewards (and thus returns) to a reasonable range so that
# training is easier
env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
if ((args.render_eval and test) or (args.render_train and not test)):
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_size = env.observation_space.low.size
action_space = env.action_space
n_atoms = 51
v_max = 500
v_min = 0
n_actions = action_space.n
q_func = q_functions.DistributionalFCStateQFunctionWithDiscreteAction(
obs_size,
n_actions,
n_atoms,
v_min,
v_max,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(args.start_epsilon,
args.end_epsilon,
args.final_exploration_steps,
action_space.sample)
opt = optimizers.Adam(1e-3)
opt.setup(q_func)
rbuf_capacity = 50000 # 5 * 10 ** 5
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(rbuf_capacity,
betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
agent = chainerrl.agents.CategoricalDQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay,
episodic_update_len=16)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
max_episode_len=timestep_limit)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--env', type=str, default='Hopper-v1')
parser.add_argument('--arch',
type=str,
default='FFGaussian',
choices=('FFSoftmax', 'FFMellowmax', 'FFGaussian'))
parser.add_argument('--normalize-obs', action='store_true')
parser.add_argument('--bound-mean', action='store_true')
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--outdir', type=str, default=None)
parser.add_argument('--steps', type=int, default=10**6)
parser.add_argument('--eval-interval', type=int, default=10000)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--update-interval', type=int, default=2048)
parser.add_argument('--batchsize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--entropy-coef', type=float, default=0.0)
args = parser.parse_args()
logging.getLogger().setLevel(args.logger_level)
if args.seed is not None:
misc.set_random_seed(args.seed)
args.outdir = experiments.prepare_output_dir(args, args.outdir)
def make_env(test):
env = gym.make(args.env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
# Scale rewards observed by agents
if args.reward_scale_factor and not test:
misc.env_modifiers.make_reward_filtered(
env, lambda x: x * args.reward_scale_factor)
if args.render:
misc.env_modifiers.make_rendered(env)
return env
sample_env = gym.make(args.env)
timestep_limit = sample_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = sample_env.observation_space
action_space = sample_env.action_space
# Switch policy types accordingly to action space types
if args.arch == 'FFSoftmax':
model = A3CFFSoftmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFMellowmax':
model = A3CFFMellowmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFGaussian':
model = A3CFFGaussian(obs_space.low.size,
action_space,
bound_mean=args.bound_mean,
normalize_obs=args.normalize_obs)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
agent = PPO(
model,
opt,
gpu=args.gpu,
phi=phi,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
if args.demo:
env = make_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = value
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.2, 0, clip_eps_setter)
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(False),
eval_env=make_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=timestep_limit,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('rom', type=str)
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames', type=int, default=10**6)
parser.add_argument('--final-epsilon', type=float, default=0.1)
parser.add_argument('--eval-epsilon', type=float, default=0.05)
parser.add_argument('--steps', type=int, default=10**7)
parser.add_argument('--replay-start-size', type=int, default=5 * 10**4)
parser.add_argument('--target-update-interval', type=int, default=10**4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--batch-size', type=int, default=32)
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for train and test envs.
train_seed = args.seed
test_seed = 2**31 - 1 - args.seed
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
# In training, life loss is considered as terminal states
env = ale.ALE(args.rom, use_sdl=args.use_sdl, seed=train_seed)
misc.env_modifiers.make_reward_clipped(env, -1, 1)
# In testing, an episode is terminated when all lives are lost
eval_env = ale.ALE(args.rom,
use_sdl=args.use_sdl,
treat_life_lost_as_terminal=False,
seed=test_seed)
n_actions = env.number_of_actions
n_atoms = 51
v_max = 10
v_min = -10
q_func = chainerrl.links.Sequence(
chainerrl.links.NatureDQNHead(),
chainerrl.q_functions.DistributionalFCStateQFunctionWithDiscreteAction(
None,
n_actions,
n_atoms,
v_min,
v_max,
n_hidden_channels=0,
n_hidden_layers=0),
)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyper parameters as https://arxiv.org/abs/1707.06887
opt = chainer.optimizers.Adam(2.5e-4, eps=1e-2 / args.batch_size)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
agent = chainerrl.agents.CategoricalDQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
batch_accumulator='mean',
phi=dqn_phi,
)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(env=eval_env,
agent=agent,
n_runs=args.eval_n_runs)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
# In testing DQN, randomly select 5% of actions
eval_explorer = explorers.ConstantEpsilonGreedy(
args.eval_epsilon, lambda: np.random.randint(n_actions))
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_explorer=eval_explorer,
save_best_so_far_agent=False,
eval_env=eval_env)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env',
type=str,
default='BreakoutNoFrameskip-v4',
help='OpenAI Atari domain to perform algorithm on.')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu',
type=int,
default=0,
help='GPU to use, set to -1 if no GPU.')
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--final-exploration-frames',
type=int,
default=10**6,
help='Timesteps after which we stop ' +
'annealing exploration rate')
parser.add_argument('--final-epsilon',
type=float,
default=0.01,
help='Final value of epsilon during training.')
parser.add_argument('--eval-epsilon',
type=float,
default=0.001,
help='Exploration epsilon used during eval episodes.')
parser.add_argument('--steps',
type=int,
default=5 * 10**7,
help='Total number of timesteps to train the agent.')
parser.add_argument(
'--max-frames',
type=int,
default=30 * 60 * 60, # 30 minutes with 60 fps
help='Maximum number of frames for each episode.')
parser.add_argument('--replay-start-size',
type=int,
default=5 * 10**4,
help='Minimum replay buffer size before ' +
'performing gradient updates.')
parser.add_argument('--target-update-interval',
type=int,
default=3 * 10**4,
help='Frequency (in timesteps) at which ' +
'the target network is updated.')
parser.add_argument('--demo-n-episodes', type=int, default=30)
parser.add_argument('--eval-n-steps', type=int, default=125000)
parser.add_argument('--eval-interval',
type=int,
default=250000,
help='Frequency (in timesteps) of evaluation phase.')
parser.add_argument('--update-interval',
type=int,
default=4,
help='Frequency (in timesteps) of network updates.')
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--render',
action='store_true',
default=False,
help='Render env states in a GUI window.')
parser.add_argument('--monitor',
action='store_true',
default=False,
help='Monitor env. Videos and additional information'
' are saved as output files.')
parser.add_argument('--lr',
type=float,
default=2.5e-4,
help='Learning rate.')
parser.add_argument('--recurrent',
action='store_true',
default=False,
help='Use a recurrent model. See the code for the'
' model definition.')
parser.add_argument('--flicker',
action='store_true',
default=False,
help='Use so-called flickering Atari, where each'
' screen is blacked out with probability 0.5.')
parser.add_argument('--no-frame-stack',
action='store_true',
default=False,
help='Disable frame stacking so that the agent can'
' only see the current screen.')
parser.add_argument('--episodic-update-len',
type=int,
default=10,
help='Maximum length of sequences for updating'
' recurrent models')
parser.add_argument('--batch-size',
type=int,
default=32,
help='Number of transitions (in a non-recurrent case)'
' or sequences (in a recurrent case) used for an'
' update.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for train and test envs.
train_seed = args.seed
test_seed = 2**31 - 1 - args.seed
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(
atari_wrappers.make_atari(args.env, max_frames=args.max_frames),
episode_life=not test,
clip_rewards=not test,
flicker=args.flicker,
frame_stack=not args.no_frame_stack,
)
env.seed(int(env_seed))
if test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, args.eval_epsilon)
if args.monitor:
env = gym.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
print('Observation space', env.observation_space)
print('Action space', env.action_space)
n_actions = env.action_space.n
if args.recurrent:
# Q-network with LSTM
q_func = chainerrl.links.StatelessRecurrentSequential(
L.Convolution2D(None, 32, 8, stride=4),
F.relu,
L.Convolution2D(None, 64, 4, stride=2),
F.relu,
L.Convolution2D(None, 64, 3, stride=1),
functools.partial(F.reshape, shape=(-1, 3136)),
F.relu,
L.NStepLSTM(1, 3136, 512, 0),
L.Linear(None, n_actions),
DiscreteActionValue,
)
# Replay buffer that stores whole episodes
rbuf = replay_buffer.EpisodicReplayBuffer(10**6)
else:
# Q-network without LSTM
q_func = chainer.Sequential(
L.Convolution2D(None, 32, 8, stride=4),
F.relu,
L.Convolution2D(None, 64, 4, stride=2),
F.relu,
L.Convolution2D(None, 64, 3, stride=1),
functools.partial(F.reshape, shape=(-1, 3136)),
L.Linear(None, 512),
F.relu,
L.Linear(None, n_actions),
DiscreteActionValue,
)
# Replay buffer that stores transitions separately
rbuf = replay_buffer.ReplayBuffer(10**6)
# Draw the computational graph and save it in the output directory.
fake_obss = np.zeros(env.observation_space.shape, dtype=np.float32)[None]
if args.recurrent:
fake_out, _ = q_func(fake_obss, None)
else:
fake_out = q_func(fake_obss)
chainerrl.misc.draw_computational_graph([fake_out],
os.path.join(args.outdir, 'model'))
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
opt = chainer.optimizers.Adam(1e-4, eps=1e-4)
opt.setup(q_func)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = chainerrl.agents.DoubleDQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
batch_accumulator='mean',
phi=phi,
minibatch_size=args.batch_size,
episodic_update_len=args.episodic_update_len,
recurrent=args.recurrent,
)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_steps=None,
n_episodes=args.demo_n_episodes,
)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.demo_n_episodes,
eval_stats['mean'],
eval_stats['median'],
eval_stats['stdev'],
))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_steps=args.eval_n_steps,
eval_n_episodes=None,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--env', type=str, default='Pendulum-v0')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--final-exploration-steps', type=int, default=10**4)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--noisy-net-sigma', type=float, default=None)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=10**5)
parser.add_argument('--prioritized-replay', action='store_true')
parser.add_argument('--replay-start-size', type=int, default=1000)
parser.add_argument('--target-update-interval', type=int, default=10**2)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--eval-interval', type=int, default=10**4)
parser.add_argument('--n-hidden-channels', type=int, default=100)
parser.add_argument('--n-hidden-layers', type=int, default=2)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--minibatch-size', type=int, default=None)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1e-3)
args = parser.parse_args()
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
args.outdir = experiments.prepare_output_dir(args,
args.outdir,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
def clip_action_filter(a):
return np.clip(a, action_space.low, action_space.high)
def make_env(test):
env = gym.make(args.env)
# Use different random seeds for train and test envs
env_seed = 2**32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor:
env = chainerrl.wrappers.Monitor(env, args.outdir)
if isinstance(env.action_space, spaces.Box):
misc.env_modifiers.make_action_filtered(env, clip_action_filter)
if not test:
# Scale rewards (and thus returns) to a reasonable range so that
# training is easier
env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
if ((args.render_eval and test) or (args.render_train and not test)):
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = env.observation_space
obs_size = obs_space.low.size
action_space = env.action_space
if isinstance(action_space, spaces.Box):
action_size = action_space.low.size
# Use NAF to apply DQN to continuous action spaces
q_func = q_functions.FCQuadraticStateQFunction(
obs_size,
action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
action_space=action_space)
# Use the Ornstein-Uhlenbeck process for exploration
ou_sigma = (action_space.high - action_space.low) * 0.2
explorer = explorers.AdditiveOU(sigma=ou_sigma)
else:
n_actions = action_space.n
q_func = q_functions.FCStateQFunctionWithDiscreteAction(
obs_size,
n_actions,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(
args.start_epsilon, args.end_epsilon, args.final_exploration_steps,
action_space.sample)
if args.noisy_net_sigma is not None:
links.to_factorized_noisy(q_func, sigma_scale=args.noisy_net_sigma)
# Turn off explorer
explorer = explorers.Greedy()
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros_like(obs_space.low, dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
opt = optimizers.Adam()
opt.setup(q_func)
rbuf_capacity = 5 * 10**5
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(rbuf_capacity,
betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
agent = DQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
train_max_episode_len=timestep_limit)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--out_dir', type=str, default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed', type=int, default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0,
help='GPU to use, set to -1 if no GPU.')
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--final-exploration-frames',
type=int, default=10 ** 5,
help='Timesteps after which we stop ' +
'annealing exploration rate')
parser.add_argument('--final-epsilon', type=float, default=0.1,
help='Final value of epsilon during training.')
parser.add_argument('--eval-epsilon', type=float, default=0.05,
help='Exploration epsilon used during eval episodes.')
parser.add_argument('--steps', type=int, default=10 ** 6,
help='Total number of timesteps to train the agent.')
parser.add_argument('--max-episode-len', type=int,
default=30 * 60 * 60 // 4, # 30 minutes with 60/4 fps
help='Maximum number of timesteps for each episode.')
parser.add_argument('--replay-start-size', type=int, default=1000,
help='Minimum replay buffer size before ' +
'performing gradient updates.')
parser.add_argument('--target-update-interval',
type=int, default=1 * 10 ** 4,
help='Frequency (in timesteps) at which ' +
'the target network is updated.')
parser.add_argument('--eval-interval', type=int, default=10 ** 5,
help='Frequency (in timesteps) of evaluation phase.')
parser.add_argument('--update-interval', type=int, default=4,
help='Frequency (in timesteps) of network updates.')
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--logging-level', type=int, default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--lr', type=float, default=2.5e-4,
help='Learning rate.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu,))
if not os.path.exists(args.out_dir):
os.makedirs(args.out_dir)
experiments.set_log_base_dir(args.out_dir)
print('Output files are saved in {}'.format(args.out_dir))
env = make_env(env_seed=args.seed)
n_actions = env.action_space.n
q_func = links.Sequence(
links.NatureDQNHead(n_input_channels=3),
L.Linear(512, n_actions),
DiscreteActionValue
)
# Use the same hyper parameters as the Nature paper's
opt = optimizers.RMSpropGraves(
lr=args.lr, alpha=0.95, momentum=0.0, eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10 ** 6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon,
args.final_exploration_frames,
lambda: np.random.randint(n_actions))
def phi(x):
# Feature extractor
x = x.transpose(2, 0, 1)
return np.asarray(x, dtype=np.float32) / 255
agent = agents.DQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
batch_accumulator='sum',
phi=phi
)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_runs=args.eval_n_runs)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.out_dir,
save_best_so_far_agent=False,
max_episode_len=args.max_episode_len,
eval_env=env,
)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--processes', type=int, default=8)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--env', type=str, default='CartPole-v0')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir', type=str, default=None)
parser.add_argument('--batchsize', type=int, default=10)
parser.add_argument('--rollout-len', type=int, default=10)
parser.add_argument('--n-hidden-channels', type=int, default=100)
parser.add_argument('--n-hidden-layers', type=int, default=2)
parser.add_argument('--n-times-replay', type=int, default=1)
parser.add_argument('--replay-start-size', type=int, default=10000)
parser.add_argument('--t-max', type=int, default=None)
parser.add_argument('--tau', type=float, default=1e-2)
parser.add_argument('--profile', action='store_true')
parser.add_argument('--steps', type=int, default=8 * 10**7)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=7e-4)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--train-async', action='store_true', default=False)
parser.add_argument('--prioritized-replay',
action='store_true',
default=False)
parser.add_argument('--disable-online-update',
action='store_true',
default=False)
parser.add_argument('--backprop-future-values',
action='store_true',
default=True)
parser.add_argument('--no-backprop-future-values',
action='store_false',
dest='backprop_future_values')
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL.
# If you use async training (--train-async), the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
if args.train_async:
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.processes) + args.seed * args.processes
assert process_seeds.max() < 2**32
args.outdir = experiments.prepare_output_dir(args, args.outdir)
def make_env(process_idx, test):
env = gym.make(args.env)
# Use different random seeds for train and test envs
if args.train_async:
process_seed = int(process_seeds[process_idx])
env_seed = 2**32 - 1 - process_seed if test else process_seed
else:
env_seed = 2**32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
if args.monitor and process_idx == 0:
env = gym.wrappers.Monitor(env, args.outdir)
# Scale rewards observed by agents
if not test:
misc.env_modifiers.make_reward_filtered(
env, lambda x: x * args.reward_scale_factor)
if args.render and process_idx == 0 and not test:
misc.env_modifiers.make_rendered(env)
return env
sample_env = gym.make(args.env)
timestep_limit = sample_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = sample_env.observation_space
action_space = sample_env.action_space
# Switch policy types accordingly to action space types
if isinstance(action_space, gym.spaces.Box):
model = chainerrl.agents.pcl.PCLSeparateModel(
pi=chainerrl.policies.FCGaussianPolicy(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high,
var_wscale=1e-3,
var_bias=1,
var_type='diagonal',
),
v=chainerrl.v_functions.FCVFunction(
obs_space.low.size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
))
else:
model = chainerrl.agents.pcl.PCLSeparateModel(
pi=chainerrl.policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers),
v=chainerrl.v_functions.FCVFunction(
obs_space.low.size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
),
)
if not args.train_async and args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
model.to_gpu(args.gpu)
if args.train_async:
opt = rmsprop_async.RMSpropAsync(lr=args.lr, alpha=0.99)
else:
opt = chainer.optimizers.Adam(alpha=args.lr)
opt.setup(model)
if args.prioritized_replay:
replay_buffer = \
chainerrl.replay_buffer.PrioritizedEpisodicReplayBuffer(
capacity=5 * 10 ** 3,
uniform_ratio=0.1,
default_priority_func=exp_return_of_episode,
wait_priority_after_sampling=False,
return_sample_weights=False)
else:
replay_buffer = chainerrl.replay_buffer.EpisodicReplayBuffer(
capacity=5 * 10**3)
agent = chainerrl.agents.PCL(
model,
opt,
replay_buffer=replay_buffer,
t_max=args.t_max,
gamma=0.99,
tau=args.tau,
phi=lambda x: x.astype(np.float32, copy=False),
rollout_len=args.rollout_len,
n_times_replay=args.n_times_replay,
replay_start_size=args.replay_start_size,
batchsize=args.batchsize,
train_async=args.train_async,
disable_online_update=args.disable_online_update,
backprop_future_values=args.backprop_future_values,
)
if args.load:
agent.load(args.load)
if args.demo:
env = make_env(0, True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
if args.train_async:
experiments.train_agent_async(agent=agent,
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
profile=args.profile,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=timestep_limit)
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(0, test=False),
eval_env=make_env(0, test=True),
outdir=args.outdir,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=timestep_limit)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument(
'--env',
type=str,
choices=[
'Pendulum-v0', 'AntBulletEnv-v0', 'HalfCheetahBulletEnv-v0',
'HumanoidBulletEnv-v0', 'HopperBulletEnv-v0',
'Walker2DBulletEnv-v0'
],
help=
'OpenAI Gym env and Pybullet (roboschool) env to perform algorithm on.'
)
parser.add_argument('--num-envs',
type=int,
default=1,
help='Number of envs run in parallel.')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--gpu',
type=int,
default=0,
help='GPU to use, set to -1 if no GPU.')
parser.add_argument('--load',
type=str,
default='',
help='Directory to load agent from.')
parser.add_argument(
'--expert-num-episode',
type=int,
default=0,
help='the number of expert trajectory, if 0, no create demo mode.')
parser.add_argument('--steps',
type=int,
default=10**6,
help='Total number of timesteps to train the agent.')
parser.add_argument('--eval-n-runs',
type=int,
default=10,
help='Number of episodes run for each evaluation.')
parser.add_argument('--eval-interval',
type=int,
default=5000,
help='Interval in timesteps between evaluations.')
parser.add_argument('--replay-start-size',
type=int,
default=10000,
help='Minimum replay buffer size before ' +
'performing gradient updates.')
parser.add_argument('--batch-size',
type=int,
default=256,
help='Minibatch size')
parser.add_argument('--render',
action='store_true',
help='Render env states in a GUI window.')
parser.add_argument('--demo',
action='store_true',
help='Just run evaluation, not training.')
parser.add_argument('--monitor',
action='store_true',
help='Wrap env with gym.wrappers.Monitor.')
parser.add_argument('--log-interval',
type=int,
default=1000,
help='Interval in timesteps between outputting log'
' messages during training')
parser.add_argument('--logger-level',
type=int,
default=logging.INFO,
help='Level of the root logger.')
parser.add_argument('--policy-output-scale',
type=float,
default=1.,
help='Weight initialization scale of polity output.')
parser.add_argument('--debug', action='store_true', help='Debug mode.')
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
if args.debug:
chainer.set_debug(True)
if args.expert_num_episode == 0:
args.outdir = experiments.prepare_output_dir(
args,
args.outdir,
argv=sys.argv,
time_format=f'{args.env}_{args.seed}')
else:
args.outdir = experiments.prepare_output_dir(
args,
args.outdir,
argv=sys.argv,
time_format=f'{args.env}_{args.expert_num_episode}expert')
args.replay_start_size = 1e8
print('Output files are saved in {}'.format(args.outdir))
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2**32
def make_env(process_idx, test):
env = gym.make(args.env)
# Unwrap TimiLimit wrapper
assert isinstance(env, gym.wrappers.TimeLimit)
env = env.env
# Use different random seeds for train and test envs
process_seed = int(process_seeds[process_idx])
env_seed = 2**32 - 1 - process_seed if test else process_seed
env.seed(env_seed)
if isinstance(env.observation_space, Box):
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
else:
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=None),
episode_life=not test,
clip_rewards=not test)
if isinstance(env.action_space, Box):
# Normalize action space to [-1, 1]^n
env = wrappers.NormalizeActionSpace(env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_batch_env(test):
return chainerrl.envs.MultiprocessVectorEnv([
functools.partial(make_env, idx, test)
for idx, env in enumerate(range(args.num_envs))
])
sample_env = make_env(process_idx=0, test=False)
timestep_limit = sample_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = sample_env.observation_space
action_space = sample_env.action_space
print('Observation space:', obs_space)
print('Action space:', action_space)
if isinstance(obs_space, Box):
head = network.FCHead()
phi = lambda x: x
else:
head = network.CNNHead(n_input_channels=4)
phi = lambda x: np.asarray(x, dtype=np.float32) / 255
if isinstance(action_space, Box):
action_size = action_space.low.size
policy = network.GaussianPolicy(copy.deepcopy(head), action_size)
q_func1 = network.QSAFunction(copy.deepcopy(head), action_size)
q_func2 = network.QSAFunction(copy.deepcopy(head), action_size)
def burnin_action_func():
"""Select random actions until model is updated one or more times."""
return np.random.uniform(action_space.low,
action_space.high).astype(np.float32)
else:
action_size = action_space.n
policy = network.SoftmaxPolicy(copy.deepcopy(head), action_size)
q_func1 = network.QSFunction(copy.deepcopy(head), action_size)
q_func2 = network.QSFunction(copy.deepcopy(head), action_size)
def burnin_action_func():
return np.random.randint(0, action_size)
policy_optimizer = optimizers.Adam(3e-4).setup(policy)
q_func1_optimizer = optimizers.Adam(3e-4).setup(q_func1)
q_func2_optimizer = optimizers.Adam(3e-4).setup(q_func2)
# Draw the computational graph and save it in the output directory.
# fake_obs = chainer.Variable(
# policy.xp.zeros_like(obs_space.low, dtype=np.float32)[None],
# name='observation')
# fake_action = chainer.Variable(
# policy.xp.zeros_like(action_space.low, dtype=np.float32)[None],
# name='action')
# chainerrl.misc.draw_computational_graph(
# [policy(fake_obs)], os.path.join(args.outdir, 'policy'))
# chainerrl.misc.draw_computational_graph(
# [q_func1(fake_obs, fake_action)], os.path.join(args.outdir, 'q_func1'))
# chainerrl.misc.draw_computational_graph(
# [q_func2(fake_obs, fake_action)], os.path.join(args.outdir, 'q_func2'))
rbuf = replay_buffer.ReplayBuffer(10**6)
# Hyperparameters in http://arxiv.org/abs/1802.09477
agent = sac.SoftActorCritic(
policy,
q_func1,
q_func2,
policy_optimizer,
q_func1_optimizer,
q_func2_optimizer,
rbuf,
gamma=0.99,
is_discrete=isinstance(action_space, Discrete),
replay_start_size=args.replay_start_size,
gpu=args.gpu,
minibatch_size=args.batch_size,
phi=phi,
burnin_action_func=burnin_action_func,
entropy_target=-action_size if isinstance(action_space, Box) else
-np.log((1.0 / action_size)) * 0.98,
temperature_optimizer=chainer.optimizers.Adam(3e-4),
)
if len(args.load) > 0:
agent.load(args.load, args.expert_num_episode == 0)
if args.demo:
eval_stats = experiments.eval_performance(
env=make_env(process_idx=0, test=True),
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit,
)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
elif args.expert_num_episode > 0:
episode_r = 0
env = sample_env
episode_len = 0
t = 0
logger = logging.getLogger(__name__)
episode_results = []
try:
for ep in range(args.expert_num_episode):
obs = env.reset()
r = 0
while True:
# a_t
action = agent.act_and_train(obs, r)
# o_{t+1}, r_{t+1}
obs, r, done, info = env.step(action)
t += 1
episode_r += r
episode_len += 1
reset = (episode_len == timestep_limit
or info.get('needs_reset', False))
if done or reset:
agent.stop_episode_and_train(obs, r, done=done)
logger.info('outdir:%s step:%s episode:%s R:%s',
args.outdir, t, ep, episode_r)
episode_results.append(episode_r)
episode_r = 0
episode_len = 0
break
logger.info('mean: %s',
sum(episode_results) / len(episode_results))
except (Exception, KeyboardInterrupt):
raise
# Save
save_name = os.path.join(
os.path.join('demos', f'{args.expert_num_episode}_episode'),
args.env)
makedirs(save_name, exist_ok=True)
agent.replay_buffer.save(os.path.join(save_name, 'replay'))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(process_idx=0, test=False),
eval_env=make_env(process_idx=0, test=True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
# log_interval=args.log_interval,
train_max_episode_len=timestep_limit,
eval_max_episode_len=timestep_limit,
)
def main():
# Prevent numpy from using multiple threads
os.environ['OMP_NUM_THREADS'] = '1'
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('rom', type=str)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--outdir', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true')
parser.add_argument('--max-episode-len', type=int, default=10000)
parser.add_argument('--profile', action='store_true')
parser.add_argument('--steps', type=int, default=8 * 10**7)
parser.add_argument('--lr', type=float, default=2.5e-4)
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
# In the original paper, agent runs in 8 environments parallely
# and samples 128 steps per environment.
# Sample 128 * 8 steps, instead.
parser.add_argument('--update-interval', type=int, default=128 * 8)
parser.add_argument('--batchsize', type=int, default=32)
parser.add_argument('--epochs', type=int, default=3)
parser.set_defaults(use_sdl=False)
args = parser.parse_args()
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
n_actions = ale.ALE(args.rom).number_of_actions
model = A3CFF(n_actions)
opt = chainer.optimizers.Adam(alpha=args.lr)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
agent = PPO(
model,
opt,
gpu=args.gpu,
phi=dqn_phi,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps=0.1,
clip_eps_vf=None,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
def make_env(test):
# Use different random seeds for train and test envs
env_seed = 2**31 - 1 - args.seed if test else args.seed
env = ale.ALE(args.rom,
use_sdl=args.use_sdl,
treat_life_lost_as_terminal=not test,
seed=env_seed)
if not test:
misc.env_modifiers.make_reward_clipped(env, -1, 1)
return env
if args.demo:
env = make_env(True)
eval_stats = experiments.eval_performance(env=env,
agent=agent,
n_runs=args.eval_n_runs)
print('n_runs: {} mean: {} median: {} stdev: {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = value
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.1, 0, clip_eps_setter)
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(False),
eval_env=make_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=args.max_episode_len,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--outdir', type=str, default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed', type=int, default=123,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--final-exploration-steps',
type=int, default=10 ** 4)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--noisy-net-sigma', type=float, default=None)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=50000)
parser.add_argument('--prioritized-replay', action='store_true', default=False)
parser.add_argument('--episodic-replay', action='store_true', default=False)
parser.add_argument('--replay-start-size', type=int, default=1000)
parser.add_argument('--target-update-interval', type=int, default=10 ** 2)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=50)
parser.add_argument('--eval-interval', type=int, default=10 ** 3)
parser.add_argument('--n-hidden-channels', type=int, default=512)
parser.add_argument('--n-hidden-layers', type=int, default=2)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--minibatch-size', type=int, default=None)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true', default=True)
parser.add_argument('--reward-scale-factor', type=float, default=1e-3)
args = parser.parse_args()
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed)
args.outdir = experiments.prepare_output_dir(
args, args.outdir, argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
ENV_NAME = 'malware-test-v0' if test else 'malware-v0'
env = gym.make(ENV_NAME)
# Use different random seeds for train and test envs
env_seed = 2 ** 32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
# if not test:
# misc.env_modifiers.make_reward_filtered(
# env, lambda x: x * args.reward_scale_factor)
if ((args.render_eval and test) or
(args.render_train and not test)):
misc.env_modifiers.make_rendered(env)
return env
env = make_env(test=False)
timestep_limit = 80
obs_space = env.observation_space
obs_size = obs_space.shape[0]
action_space = env.action_space
n_actions = action_space.n
q_func = q_functions.FCStateQFunctionWithDiscreteAction(
obs_size, n_actions,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
if args.gpu >= 0:
q_func.to_gpu(args.gpu)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(
args.start_epsilon, args.end_epsilon, args.final_exploration_steps,
action_space.sample)
if args.noisy_net_sigma is not None:
links.to_factorized_noisy(q_func)
# Turn off explorer
explorer = explorers.Greedy()
# Draw the computational graph and save it in the output directory.
if args.gpu < 0:
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros_like(obs_space, dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
opt = optimizers.Adam()
opt.setup(q_func)
rbuf_capacity = 5 * 10 ** 5
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
def phi(obs):
return obs.astype(np.float32)
agent = DoubleDQN(q_func, opt, rbuf, gamma=args.gamma,
explorer=explorer, replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
phi=phi, minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay, episodic_update_len=16)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
q_hook = PlotHook('Average Q Value')
loss_hook = PlotHook('Average Loss', plot_index=1)
experiments.train_agent_with_evaluation(
agent=agent, env=env, steps=args.steps,
eval_n_runs=args.eval_n_runs, eval_interval=args.eval_interval,
outdir=args.outdir, eval_env=eval_env,
max_episode_len=timestep_limit,
step_hooks=[q_hook, loss_hook],
successful_score=7
)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--env', type=str, default='Humanoid-v2')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--final-exploration-steps', type=int, default=10**6)
parser.add_argument('--actor-lr', type=float, default=1e-4)
parser.add_argument('--critic-lr', type=float, default=1e-3)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--steps', type=int, default=10**7)
parser.add_argument('--n-hidden-channels', type=int, default=300)
parser.add_argument('--n-hidden-layers', type=int, default=3)
parser.add_argument('--replay-start-size', type=int, default=5000)
parser.add_argument('--n-update-times', type=int, default=1)
parser.add_argument('--target-update-interval', type=int, default=1)
parser.add_argument('--target-update-method',
type=str,
default='soft',
choices=['hard', 'soft'])
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--gamma', type=float, default=0.995)
parser.add_argument('--minibatch-size', type=int, default=200)
parser.add_argument('--render', action='store_true')
parser.add_argument('--demo', action='store_true')
parser.add_argument('--use-bn', action='store_true', default=False)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
args = parser.parse_args()
args.outdir = experiments.prepare_output_dir(args,
args.outdir,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
def clip_action_filter(a):
return np.clip(a, action_space.low, action_space.high)
def reward_filter(r):
return r * args.reward_scale_factor
def make_env(test):
env = gym.make(args.env)
# Use different random seeds for train and test envs
env_seed = 2**32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor:
env = chainerrl.wrappers.Monitor(env, args.outdir)
if isinstance(env.action_space, spaces.Box):
misc.env_modifiers.make_action_filtered(env, clip_action_filter)
if not test:
# Scale rewards (and thus returns) to a reasonable range so that
# training is easier
env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
if args.render and not test:
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_size = np.asarray(env.observation_space.shape).prod()
action_space = env.action_space
action_size = np.asarray(action_space.shape).prod()
if args.use_bn:
q_func = q_functions.FCBNLateActionSAQFunction(
obs_size,
action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
normalize_input=True)
pi = policy.FCBNDeterministicPolicy(
obs_size,
action_size=action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
min_action=action_space.low,
max_action=action_space.high,
bound_action=True,
normalize_input=True)
else:
q_func = q_functions.FCSAQFunction(
obs_size,
action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
pi = policy.FCDeterministicPolicy(
obs_size,
action_size=action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
min_action=action_space.low,
max_action=action_space.high,
bound_action=True)
model = DDPGModel(q_func=q_func, policy=pi)
opt_a = optimizers.Adam(alpha=args.actor_lr)
opt_c = optimizers.Adam(alpha=args.critic_lr)
opt_a.setup(model['policy'])
opt_c.setup(model['q_function'])
opt_a.add_hook(chainer.optimizer.GradientClipping(1.0), 'hook_a')
opt_c.add_hook(chainer.optimizer.GradientClipping(1.0), 'hook_c')
rbuf = replay_buffer.ReplayBuffer(5 * 10**5)
def random_action():
a = action_space.sample()
if isinstance(a, np.ndarray):
a = a.astype(np.float32)
return a
ou_sigma = (action_space.high - action_space.low) * 0.2
explorer = explorers.AdditiveOU(sigma=ou_sigma)
agent = DDPG(model,
opt_a,
opt_c,
rbuf,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_method=args.target_update_method,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
soft_update_tau=args.soft_update_tau,
n_times_update=args.n_update_times,
gpu=args.gpu,
minibatch_size=args.minibatch_size)
if len(args.load) > 0:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_env=eval_env,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
train_max_episode_len=timestep_limit)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('--outdir', type=str, default='out')
parser.add_argument('--env', type=str, default='Humanoid-v1')
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--final-exploration-steps', type=int, default=10**6)
parser.add_argument('--actor-lr', type=float, default=1e-4)
parser.add_argument('--critic-lr', type=float, default=1e-3)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--steps', type=int, default=10**7)
parser.add_argument('--n-hidden-channels', type=int, default=300)
parser.add_argument('--n-hidden-layers', type=int, default=3)
parser.add_argument('--replay-start-size', type=int, default=5000)
parser.add_argument('--n-update-times', type=int, default=1)
parser.add_argument('--target-update-frequency', type=int, default=1)
parser.add_argument('--target-update-method',
type=str,
default='soft',
choices=['hard', 'soft'])
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-frequency', type=int, default=4)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--eval-frequency', type=int, default=10**5)
parser.add_argument('--gamma', type=float, default=0.995)
parser.add_argument('--minibatch-size', type=int, default=200)
parser.add_argument('--render', action='store_true')
parser.add_argument('--demo', action='store_true')
parser.add_argument('--use-bn', action='store_true', default=False)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
args = parser.parse_args()
args.outdir = experiments.prepare_output_dir(args,
args.outdir,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
if args.seed is not None:
misc.set_random_seed(args.seed)
def clip_action_filter(a):
return np.clip(a, action_space.low, action_space.high)
def reward_filter(r):
return r * args.reward_scale_factor
def make_env():
env = gym.make(args.env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if isinstance(env.action_space, spaces.Box):
misc.env_modifiers.make_action_filtered(env, clip_action_filter)
misc.env_modifiers.make_reward_filtered(env, reward_filter)
if args.render:
misc.env_modifiers.make_rendered(env)
def __exit__(self, *args):
pass
env.__exit__ = __exit__
return env
env = make_env()
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_size = np.asarray(env.observation_space.shape).prod()
action_space = env.action_space
action_size = np.asarray(action_space.shape).prod()
if args.use_bn:
q_func = q_functions.FCBNLateActionSAQFunction(
obs_size,
action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
normalize_input=True)
pi = policy.FCBNDeterministicPolicy(
obs_size,
action_size=action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
min_action=action_space.low,
max_action=action_space.high,
bound_action=True,
normalize_input=True)
else:
q_func = q_functions.FCSAQFunction(
obs_size,
action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
pi = policy.FCDeterministicPolicy(
obs_size,
action_size=action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
min_action=action_space.low,
max_action=action_space.high,
bound_action=True)
model = DDPGModel(q_func=q_func, policy=pi)
opt_a = optimizers.Adam(alpha=args.actor_lr)
opt_c = optimizers.Adam(alpha=args.critic_lr)
opt_a.setup(model['policy'])
opt_c.setup(model['q_function'])
opt_a.add_hook(chainer.optimizer.GradientClipping(1.0), 'hook_a')
opt_c.add_hook(chainer.optimizer.GradientClipping(1.0), 'hook_c')
rbuf = replay_buffer.ReplayBuffer(5 * 10**5)
def phi(obs):
return obs.astype(np.float32)
def random_action():
a = action_space.sample()
if isinstance(a, np.ndarray):
a = a.astype(np.float32)
return a
ou_sigma = (action_space.high - action_space.low) * 0.2
explorer = explorers.AdditiveOU(sigma=ou_sigma)
agent = DDPG(model,
opt_a,
opt_c,
rbuf,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_method=args.target_update_method,
target_update_frequency=args.target_update_frequency,
update_frequency=args.update_frequency,
soft_update_tau=args.soft_update_tau,
n_times_update=args.n_update_times,
phi=phi,
gpu=args.gpu,
minibatch_size=args.minibatch_size)
agent.logger.setLevel(logging.DEBUG)
if len(args.load) > 0:
agent.load(args.load)
if args.demo:
mean, median, stdev = experiments.eval_performance(
env=env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev'.format(
args.eval_n_runs, mean, median, stdev))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_frequency=args.eval_frequency,
outdir=args.outdir,
max_episode_len=timestep_limit)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='CartPole-v0')
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--outdir', type=str, default='results')
parser.add_argument('--beta', type=float, default=1e-4)
parser.add_argument('--batchsize', type=int, default=10)
parser.add_argument('--steps', type=int, default=10**5)
parser.add_argument('--eval-interval', type=int, default=10**4)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
args = parser.parse_args()
logging.getLogger().setLevel(args.logger_level)
if args.seed is not None:
misc.set_random_seed(args.seed)
args.outdir = experiments.prepare_output_dir(args, args.outdir)
def make_env(test):
env = gym.make(args.env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
# Scale rewards observed by agents
if not test:
misc.env_modifiers.make_reward_filtered(
env, lambda x: x * args.reward_scale_factor)
if args.render and not test:
misc.env_modifiers.make_rendered(env)
return env
train_env = make_env(test=False)
timestep_limit = train_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = train_env.observation_space
action_space = train_env.action_space
# Switch policy types accordingly to action space types
if isinstance(action_space, gym.spaces.Box):
model = chainerrl.policies.FCGaussianPolicyWithFixedCovariance(
obs_space.low.size,
action_space.low.size,
var=0.1,
n_hidden_channels=200,
n_hidden_layers=2,
nonlinearity=chainer.functions.leaky_relu,
)
else:
model = chainerrl.policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=200,
n_hidden_layers=2,
nonlinearity=chainer.functions.leaky_relu,
)
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
model.to_gpu(args.gpu)
opt = chainer.optimizers.Adam(alpha=args.lr)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(1))
agent = chainerrl.agents.REINFORCE(model,
opt,
beta=args.beta,
phi=phi,
batchsize=args.batchsize)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=train_env,
eval_env=eval_env,
outdir=args.outdir,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=timestep_limit)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='CartPole-v0')
parser.add_argument('--seed', type=int, default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--outdir', type=str, default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--beta', type=float, default=1e-4)
parser.add_argument('--batchsize', type=int, default=10)
parser.add_argument('--steps', type=int, default=10 ** 5)
parser.add_argument('--eval-interval', type=int, default=10 ** 4)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=1e-3)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu,))
args.outdir = experiments.prepare_output_dir(args, args.outdir)
def make_env(test):
env = gym.make(args.env)
# Use different random seeds for train and test envs
env_seed = 2 ** 32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if not test:
# Scale rewards (and thus returns) to a reasonable range so that
# training is easier
env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
if args.render and not test:
env = chainerrl.wrappers.Render(env)
return env
train_env = make_env(test=False)
timestep_limit = train_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = train_env.observation_space
action_space = train_env.action_space
# Switch policy types accordingly to action space types
if isinstance(action_space, gym.spaces.Box):
model = chainerrl.policies.FCGaussianPolicyWithFixedCovariance(
obs_space.low.size,
action_space.low.size,
var=0.1,
n_hidden_channels=200,
n_hidden_layers=2,
nonlinearity=chainer.functions.leaky_relu,
)
else:
model = chainerrl.policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=200,
n_hidden_layers=2,
nonlinearity=chainer.functions.leaky_relu,
)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[model(np.zeros_like(obs_space.low, dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
model.to_gpu(args.gpu)
opt = chainer.optimizers.Adam(alpha=args.lr)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(1))
agent = chainerrl.agents.REINFORCE(
model, opt, beta=args.beta, batchsize=args.batchsize)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=train_env,
eval_env=eval_env,
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
train_max_episode_len=timestep_limit)
