def parse_arch(arch, n_actions, activation):
if arch == 'nature':
return links.Sequence(links.NatureDQNHead(activation=activation),
L.Linear(512, n_actions), DiscreteActionValue)
elif arch == 'nips':
return links.Sequence(links.NIPSDQNHead(activation=activation),
L.Linear(256, n_actions), DiscreteActionValue)
elif arch == 'dueling':
return DuelingDQN(n_actions)
else:
raise RuntimeError('Not supported architecture: {}'.format(arch))
def parse_arch(arch, n_actions):
if arch == 'nature':
return links.Sequence(links.NatureDQNHead(), L.Linear(512, n_actions),
DiscreteActionValue)
elif arch == 'doubledqn':
return links.Sequence(links.NatureDQNHead(),
L.Linear(512, n_actions, nobias=True),
SingleSharedBias(), DiscreteActionValue)
elif arch == 'nips':
return links.Sequence(links.NIPSDQNHead(), L.Linear(256, n_actions),
DiscreteActionValue)
elif arch == 'dueling':
return DuelingDQN(n_actions)
else:
raise RuntimeError('Not supported architecture: {}'.format(arch))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('processes', type=int)
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
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('--t-max', type=int, default=5)
parser.add_argument('--replay-start-size', type=int, default=10000)
parser.add_argument('--n-times-replay', type=int, default=4)
parser.add_argument('--beta', type=float, default=1e-2)
parser.add_argument('--profile', action='store_true')
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('--lr', type=float, default=7e-4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--use-lstm', action='store_true')
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
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.set_defaults(use_lstm=False)
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# 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**31
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
n_actions = gym.make(args.env).action_space.n
if args.use_lstm:
model = acer.ACERSharedModel(
shared=links.Sequence(links.NIPSDQNHead(), L.LSTM(256, 256)),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
else:
model = acer.ACERSharedModel(
shared=links.NIPSDQNHead(),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=4e-3, alpha=0.99)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
replay_buffer = EpisodicReplayBuffer(10**6 // args.processes)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = acer.ACER(model,
opt,
t_max=args.t_max,
gamma=0.99,
replay_buffer=replay_buffer,
n_times_replay=args.n_times_replay,
replay_start_size=args.replay_start_size,
beta=args.beta,
phi=phi)
if args.load:
agent.load(args.load)
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_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 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
if args.demo:
env = make_env(0, 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.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
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,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=False,
)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('processes', type=int)
parser.add_argument('rom', type=str)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
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('--use-sdl', action='store_true')
parser.add_argument('--t-max', type=int, default=5)
parser.add_argument('--replay-start-size', type=int, default=10000)
parser.add_argument('--n-times-replay', type=int, default=4)
parser.add_argument('--max-episode-len', type=int, default=10000)
parser.add_argument('--beta', 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('--lr', type=float, default=7e-4)
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--use-lstm', action='store_true')
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.set_defaults(use_sdl=False)
parser.set_defaults(use_lstm=False)
args = parser.parse_args()
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# 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**31
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
if args.use_lstm:
model = acer.ACERSharedModel(
shared=links.Sequence(links.NIPSDQNHead(), L.LSTM(256, 256)),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
else:
model = acer.ACERSharedModel(
shared=links.NIPSDQNHead(),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=4e-3, alpha=0.99)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
replay_buffer = EpisodicReplayBuffer(10**6 // args.processes)
agent = acer.ACER(model,
opt,
t_max=args.t_max,
gamma=0.99,
replay_buffer=replay_buffer,
n_times_replay=args.n_times_replay,
replay_start_size=args.replay_start_size,
beta=args.beta,
phi=dqn_phi)
if args.load:
agent.load(args.load)
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_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(0, 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.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
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=args.max_episode_len,
global_step_hooks=[lr_decay_hook])
def __init__(self, n_actions):
self.head = links.NIPSDQNHead()
self.pi = policy.FCSoftmaxPolicy(self.head.n_output_channels,
n_actions)
self.v = v_function.FCVFunction(self.head.n_output_channels)
super().__init__(self.head, self.pi, self.v)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('processes', type=int)
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--lr', type=float, default=7e-4)
parser.add_argument('--steps', type=int, default=8 * 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('--final-exploration-frames',
type=int,
default=4 * 10**6)
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('--profile', action='store_true')
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
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.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# 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**31
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_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 test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, 0.05)
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
sample_env = make_env(0, test=False)
action_space = sample_env.action_space
assert isinstance(action_space, spaces.Discrete)
# Define a model and its optimizer
q_func = links.Sequence(links.NIPSDQNHead(), L.Linear(256, action_space.n),
DiscreteActionValue)
opt = rmsprop_async.RMSpropAsync(lr=args.lr, eps=1e-1, alpha=0.99)
opt.setup(q_func)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
# Make process-specific agents to diversify exploration
def make_agent(process_idx):
# Random epsilon assignment described in the original paper
rand = random.random()
if rand < 0.4:
epsilon_target = 0.1
elif rand < 0.7:
epsilon_target = 0.01
else:
epsilon_target = 0.5
explorer = explorers.LinearDecayEpsilonGreedy(
1, epsilon_target, args.final_exploration_frames,
action_space.sample)
# Suppress the explorer logger
explorer.logger.setLevel(logging.INFO)
return nsq.NSQ(q_func,
opt,
t_max=5,
gamma=0.99,
i_target=40000,
explorer=explorer,
phi=phi)
if args.demo:
env = make_env(0, True)
agent = make_agent(0)
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.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
make_agent=make_agent,
profile=args.profile,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=args.max_episode_len,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=False,
)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('processes', type=int)
parser.add_argument('rom', type=str)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--lr', type=float, default=7e-4)
parser.add_argument('--steps', type=int, default=8 * 10**7)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames',
type=int,
default=4 * 10**6)
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('--profile', action='store_true')
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
args = parser.parse_args()
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# 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**31
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_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
sample_env = make_env(0, test=False)
action_space = sample_env.action_space
assert isinstance(action_space, spaces.Discrete)
# Define a model and its optimizer
q_func = links.Sequence(links.NIPSDQNHead(), L.Linear(256, action_space.n),
DiscreteActionValue)
opt = rmsprop_async.RMSpropAsync(lr=args.lr, eps=1e-1, alpha=0.99)
opt.setup(q_func)
# Make process-specific agents to diversify exploration
def make_agent(process_idx):
# Random epsilon assignment described in the original paper
rand = random.random()
if rand < 0.4:
epsilon_target = 0.1
elif rand < 0.7:
epsilon_target = 0.01
else:
epsilon_target = 0.5
explorer = explorers.LinearDecayEpsilonGreedy(
1, epsilon_target, args.final_exploration_frames,
action_space.sample)
# Suppress the explorer logger
explorer.logger.setLevel(logging.INFO)
return nsq.NSQ(q_func,
opt,
t_max=5,
gamma=0.99,
i_target=40000,
explorer=explorer,
phi=dqn_phi)
if args.demo:
env = make_env(0, True)
agent = make_agent(0)
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:
explorer = explorers.ConstantEpsilonGreedy(0.05, action_space.sample)
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(outdir=args.outdir,
processes=args.processes,
make_env=make_env,
make_agent=make_agent,
profile=args.profile,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
eval_explorer=explorer,
global_step_hooks=[lr_decay_hook])
def main():
# This prevents numpy from using multiple threads
os.environ['OMP_NUM_THREADS'] = '1'
import logging
# logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('processes', type=int)
parser.add_argument('rom', type=str)
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--lr', type=float, default=7e-4)
parser.add_argument('--steps', type=int, default=8 * 10**7)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames',
type=int,
default=4 * 10**6)
parser.add_argument('--outdir', type=str, default='nsq_output')
parser.add_argument('--profile', action='store_true')
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
args = parser.parse_args()
if args.seed is not None:
misc.set_random_seed(args.seed)
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(process_idx, test):
env = ale.ALE(args.rom,
use_sdl=args.use_sdl,
treat_life_lost_as_terminal=not test)
if not test:
misc.env_modifiers.make_reward_clipped(env, -1, 1)
return env
sample_env = make_env(0, test=False)
action_space = sample_env.action_space
assert isinstance(action_space, spaces.Discrete)
# Define a model and its optimizer
q_func = links.Sequence(links.NIPSDQNHead(), L.Linear(256, action_space.n),
DiscreteActionValue)
opt = rmsprop_async.RMSpropAsync(lr=args.lr, eps=1e-1, alpha=0.99)
opt.setup(q_func)
# Make process-specific agents to diversify exploration
def make_agent(process_idx):
# Random epsilon assignment described in the original paper
rand = random.random()
if rand < 0.4:
epsilon_target = 0.1
elif rand < 0.7:
epsilon_target = 0.01
else:
epsilon_target = 0.5
explorer = explorers.LinearDecayEpsilonGreedy(
1, epsilon_target, args.final_exploration_frames,
action_space.sample)
# Suppress the explorer logger
explorer.logger.setLevel(logging.INFO)
return nsq.NSQ(q_func,
opt,
t_max=5,
gamma=0.99,
i_target=40000,
explorer=explorer,
phi=dqn_phi)
if args.demo:
env = make_env(0, True)
agent = make_agent(0)
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:
explorer = explorers.ConstantEpsilonGreedy(0.05, action_space.sample)
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(outdir=args.outdir,
processes=args.processes,
make_env=make_env,
make_agent=make_agent,
profile=args.profile,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
eval_explorer=explorer,
global_step_hooks=[lr_decay_hook])
def main(args):
import logging
logging.basicConfig(level=logging.INFO, filename='log')
if (type(args) is list):
args = make_args(args)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# 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**31
n_actions = gym.make(args.env).action_space.n
if args.use_lstm:
model = acer.ACERSharedModel(
shared=links.Sequence(links.NIPSDQNHead(), L.LSTM(256, 256)),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
else:
model = acer.ACERSharedModel(
shared=links.NIPSDQNHead(),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=4e-3, alpha=0.99)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
replay_buffer = EpisodicReplayBuffer(10**6 // args.processes)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = acer.ACER(model,
opt,
t_max=args.t_max,
gamma=0.99,
replay_buffer=replay_buffer,
n_times_replay=args.n_times_replay,
replay_start_size=args.replay_start_size,
beta=args.beta,
phi=phi)
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_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 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
def make_env_check():
# Use different random seeds for train and test envs
env_seed = args.seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=True,
clip_rewards=True)
env.seed(int(env_seed))
return env
if args.load_agent:
agent.load(args.load_agent)
if (args.mode == 'train'):
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(
agent=agent,
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
profile=args.profile,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=False,
)
elif (args.mode == 'check'):
return tools.make_video.check(env=make_env_check(),
agent=agent,
save_mp4=args.save_mp4)
elif (args.mode == 'growth'):
return tools.make_video.growth(env=make_env_check(),
agent=agent,
outdir=args.outdir,
max_num=args.max_frames,
save_mp4=args.save_mp4)
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('processes', type=int)
parser.add_argument('rom', type=str)
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--outdir', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true')
parser.add_argument('--t-max', type=int, default=5)
parser.add_argument('--replay-start-size', type=int, default=10000)
parser.add_argument('--n-times-replay', type=int, default=4)
parser.add_argument('--max-episode-len', type=int, default=10000)
parser.add_argument('--beta', 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('--lr', type=float, default=7e-4)
parser.add_argument('--eval-frequency', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--use-lstm', action='store_true')
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.set_defaults(use_sdl=False)
parser.set_defaults(use_lstm=False)
args = parser.parse_args()
if args.seed is not None:
misc.set_random_seed(args.seed)
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
if args.use_lstm:
model = acer.ACERSharedModel(
shared=links.Sequence(links.NIPSDQNHead(), L.LSTM(256, 256)),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
else:
model = acer.ACERSharedModel(
shared=links.NIPSDQNHead(),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=4e-3, alpha=0.99)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
replay_buffer = EpisodicReplayBuffer(10**6 // args.processes)
agent = acer.ACER(model,
opt,
t_max=args.t_max,
gamma=0.99,
replay_buffer=replay_buffer,
n_times_replay=args.n_times_replay,
replay_start_size=args.replay_start_size,
beta=args.beta,
phi=dqn_phi)
if args.load:
agent.load(args.load)
def make_env(process_idx, test):
env = ale.ALE(args.rom,
use_sdl=args.use_sdl,
treat_life_lost_as_terminal=not test)
if not test:
misc.env_modifiers.make_reward_clipped(env, -1, 1)
return env
if args.demo:
env = make_env(0, True)
mean, median, stdev = experiments.eval_performance(
env=env, agent=agent, n_runs=args.eval_n_runs)
print('n_runs: {} mean: {} median: {} stdev'.format(
args.eval_n_runs, mean, median, stdev))
else:
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_frequency=args.eval_frequency,
max_episode_len=args.max_episode_len)
def __init__(self, alg, env, model_path):
self.alg = alg
seed = 0
n_actions = gym.make(env).action_space.n
gpus = [-1]
gpu = None
misc.set_random_seed(seed, gpus=gpus)
if alg == "DQN-C":
model = links.Sequence(
links.NatureDQNHead(),
L.Linear(512, n_actions),
DiscreteActionValue)
if alg == "PPO":
winit_last = chainer.initializers.LeCunNormal(1e-2)
model = 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),
F.relu,
L.Linear(None, 512),
F.relu,
links.Branched(
chainer.Sequential(
L.Linear(None, n_actions, initialW=winit_last),
SoftmaxDistribution,
),
L.Linear(None, 1),
)
)
if alg == "C51":
n_atoms = 51
v_max = 10
v_min = -10
model = links.Sequence(
links.NatureDQNHead(),
DistributionalFCStateQFunctionWithDiscreteAction(
None, n_actions, n_atoms, v_min, v_max,
n_hidden_channels=0, n_hidden_layers=0),
)
if alg == "ACER":
model = agents.acer.ACERSharedModel(
shared=links.Sequence(
links.NIPSDQNHead(),
L.LSTM(256, 256)),
pi=links.Sequence(
L.Linear(256, n_actions),
SoftmaxDistribution),
q=links.Sequence(
L.Linear(256, n_actions),
DiscreteActionValue),
)
if alg == "A3C":
model = A3CFF(n_actions)
if alg == "Rainbow":
n_atoms = 51
v_max = 10
v_min = -10
model = DistributionalDuelingDQN(n_actions, n_atoms, v_min, v_max)
links.to_factorized_noisy(model, sigma_scale=0.5)
if alg == "IQN":
model = agents.iqn.ImplicitQuantileQFunction(
psi=chainerrl.links.Sequence(
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),
F.relu,
functools.partial(F.reshape, shape=(-1, 3136)),
),
phi=chainerrl.links.Sequence(
chainerrl.agents.iqn.CosineBasisLinear(64, 3136),
F.relu,
),
f=chainerrl.links.Sequence(
L.Linear(None, 512),
F.relu,
L.Linear(None, n_actions),
),
)
if alg in ["A3C"]:
fake_obs = chainer.Variable(
np.zeros((4, 84, 84), dtype=np.float32)[None],
name='observation')
with chainerrl.recurrent.state_reset(model):
# The state of the model is reset again after drawing the graph
variables = misc.collect_variables([model(fake_obs)])
chainer.computational_graph.build_computational_graph(variables)
elif alg in ["Rainbow", "DQN-C", "C51", "ACER", "PPO"]:
variables = misc.collect_variables([model(np.zeros((4, 84, 84), dtype=np.float32)[None])])
chainer.computational_graph.build_computational_graph(variables)
else:
fake_obs = np.zeros((4, 84, 84), dtype=np.float32)[None]
fake_taus = np.zeros(32, dtype=np.float32)[None]
variables = misc.collect_variables([model(fake_obs)(fake_taus)])
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
opt = optimizers.RMSpropGraves()
opt.setup(model)
rbuf = replay_buffer.ReplayBuffer(1)
if alg == "IQN":
self.agent = agents.IQN(model, opt, rbuf, gpu=gpu, gamma=0.99, act_deterministically=True, explorer=None,
replay_start_size=1, minibatch_size=1, target_update_interval=None, clip_delta=True,
update_interval=4, phi=phi)
if alg == "A3C":
self.agent = a3c.A3C(model, opt, t_max=5, gamma=0.99, phi=phi, act_deterministically=True)
if alg == "Rainbow":
self.agent = agents.CategoricalDoubleDQN(model, opt, rbuf, gpu=gpu, gamma=0.99, explorer=None,
replay_start_size=1, minibatch_size=1, target_update_interval=None,
clip_delta=True, update_interval=4, phi=phi)
if alg == "DQN-C":
self.agent = agents.DQN(model, opt, rbuf, gpu=gpu, gamma=0.99, explorer=None, replay_start_size=1,
minibatch_size=1, target_update_interval=None, clip_delta=True, update_interval=4,
phi=phi)
if alg == "C51":
self.agent = agents.CategoricalDQN(
model, opt, rbuf, gpu=gpu, gamma=0.99,
explorer=None, replay_start_size=1,
minibatch_size=1,
target_update_interval=None,
clip_delta=True,
update_interval=4,
phi=phi,
)
if alg == "ACER":
self.agent = agents.acer.ACER(model, opt, t_max=5, gamma=0.99,
replay_buffer=rbuf,
n_times_replay=4,
replay_start_size=1,
act_deterministically=True,
phi=phi
)
if alg == "PPO":
self.agent = agents.PPO(model, opt, gpu=gpu, phi=phi, update_interval=4, minibatch_size=1, clip_eps=0.1,
recurrent=False, act_deterministically=True)
self.agent.load(os.path.join(model_path, 'chainer', alg, env.replace("NoFrameskip-v4", ""), 'final'))
def main(args):
import logging
logging.basicConfig(level=logging.INFO, filename='log')
if (type(args) is list):
args = make_args(args)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# 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**31
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_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, 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
sample_env = make_env(0, test=False)
action_space = sample_env.action_space
assert isinstance(action_space, spaces.Discrete)
# Define a model and its optimizer
q_func = links.Sequence(links.NIPSDQNHead(), L.Linear(256, action_space.n),
DiscreteActionValue)
opt = rmsprop_async.RMSpropAsync(lr=args.lr, eps=1e-1, alpha=0.99)
opt.setup(q_func)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
# Make process-specific agents to diversify exploration
def make_agent(process_idx):
# Random epsilon assignment described in the original paper
rand = random.random()
if rand < 0.4:
epsilon_target = 0.1
elif rand < 0.7:
epsilon_target = 0.01
else:
epsilon_target = 0.5
explorer = explorers.LinearDecayEpsilonGreedy(
1, epsilon_target, args.final_exploration_frames,
action_space.sample)
# Suppress the explorer logger
explorer.logger.setLevel(logging.INFO)
return nsq.NSQ(q_func,
opt,
t_max=5,
gamma=0.99,
i_target=40000,
explorer=explorer,
phi=phi)
if args.demo:
env = make_env(0, True)
agent = make_agent(0)
eval_stats = experiments.eval_performance(env=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:
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
make_agent=make_agent,
profile=args.profile,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=False,
)
