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("--use-lstm", action="store_true")
parser.add_argument("--demo", action="store_true", default=False)
parser.add_argument("--load", type=str, default="")
parser.add_argument(
"--log-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.log_level)
# Set a random seed used in PFRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
utils.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
input_to_hidden = nn.Sequential(
nn.Conv2d(4, 16, 8, stride=4),
nn.ReLU(),
nn.Conv2d(16, 32, 4, stride=2),
nn.ReLU(),
nn.Flatten(),
nn.Linear(2592, 256),
nn.ReLU(),
)
head = acer.ACERDiscreteActionHead(
pi=nn.Sequential(
nn.Linear(256, n_actions),
SoftmaxCategoricalHead(),
),
q=nn.Sequential(
nn.Linear(256, n_actions),
DiscreteActionValueHead(),
),
)
if args.use_lstm:
model = pfrl.nn.RecurrentSequential(
input_to_hidden,
nn.LSTM(num_layers=1, input_size=256, hidden_size=256),
head,
)
else:
model = nn.Sequential(input_to_hidden, head)
model.apply(pfrl.initializers.init_chainer_default)
opt = pfrl.optimizers.SharedRMSpropEpsInsideSqrt(model.parameters(),
lr=args.lr,
eps=4e-3,
alpha=0.99)
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,
max_grad_norm=40,
recurrent=args.use_lstm,
)
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 = pfrl.wrappers.Monitor(
env, args.outdir, mode="evaluation" if test else "training")
if args.render:
env = pfrl.wrappers.Render(env)
return env
if args.demo:
env = make_env(0, True)
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):
for pg in agent.optimizer.param_groups:
assert "lr" in pg
pg["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,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--processes", type=int, default=16)
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("--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(
"--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=250000)
parser.add_argument("--eval-n-steps", type=int, default=125000)
parser.add_argument("--demo", action="store_true", default=False)
parser.add_argument("--load-pretrained",
action="store_true",
default=False)
parser.add_argument("--load", type=str, default="")
parser.add_argument(
"--log-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.log_level)
# Set a random seed used in PFRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
utils.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, max_frames=args.max_frames),
episode_life=not test,
clip_rewards=not test,
)
env.seed(int(env_seed))
if args.monitor:
env = pfrl.wrappers.Monitor(
env, args.outdir, mode="evaluation" if test else "training")
if args.render:
env = pfrl.wrappers.Render(env)
return env
sample_env = make_env(0, False)
obs_size = sample_env.observation_space.low.shape[0]
n_actions = sample_env.action_space.n
model = nn.Sequential(
nn.Conv2d(obs_size, 16, 8, stride=4),
nn.ReLU(),
nn.Conv2d(16, 32, 4, stride=2),
nn.ReLU(),
nn.Flatten(),
nn.Linear(2592, 256),
nn.ReLU(),
pfrl.nn.Branched(
nn.Sequential(
nn.Linear(256, n_actions),
SoftmaxCategoricalHead(),
),
nn.Linear(256, 1),
),
)
# SharedRMSprop is same as torch.optim.RMSprop except that it initializes
# its state in __init__, allowing it to be moved to shared memory.
opt = SharedRMSpropEpsInsideSqrt(model.parameters(),
lr=7e-4,
eps=1e-1,
alpha=0.99)
assert opt.state_dict()["state"], (
"To share optimizer state across processes, the state must be"
" initialized before training.")
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = a3c.A3C(
model,
opt,
t_max=args.t_max,
gamma=0.99,
beta=args.beta,
phi=phi,
max_grad_norm=40.0,
)
if args.load_pretrained:
raise Exception("Pretrained models are currently unsupported.")
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_steps=args.eval_n_steps,
n_episodes=None)
print("n_steps: {} mean: {} median: {} stdev: {}".format(
args.eval_n_steps,
eval_stats["mean"],
eval_stats["median"],
eval_stats["stdev"],
))
else:
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
for pg in agent.optimizer.param_groups:
assert "lr" in pg
pg["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=args.eval_n_steps,
eval_n_episodes=None,
eval_interval=args.eval_interval,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=True,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--env",
type=str,
default="BreakoutNoFrameskip-v4",
help="Gym Env ID.")
parser.add_argument("--gpu",
type=int,
default=0,
help="GPU device ID. Set to -1 to use CPUs only.")
parser.add_argument(
"--num-envs",
type=int,
default=8,
help="Number of env instances run in parallel.",
)
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**7,
help="Total time steps for training.")
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=2.5e-4,
help="Learning rate.")
parser.add_argument(
"--eval-interval",
type=int,
default=100000,
help="Interval (in timesteps) between evaluation phases.",
)
parser.add_argument(
"--eval-n-runs",
type=int,
default=10,
help="Number of episodes ran in an evaluation phase.",
)
parser.add_argument(
"--demo",
action="store_true",
default=False,
help="Run demo episodes, not training.",
)
parser.add_argument(
"--load",
type=str,
default="",
help=("Directory path to load a saved agent data from"
" if it is a non-empty string."),
)
parser.add_argument(
"--log-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(
"--update-interval",
type=int,
default=128 * 8,
help="Interval (in timesteps) between PPO iterations.",
)
parser.add_argument(
"--batchsize",
type=int,
default=32 * 8,
help="Size of minibatch (in timesteps).",
)
parser.add_argument(
"--epochs",
type=int,
default=4,
help="Number of epochs used for each PPO iteration.",
)
parser.add_argument(
"--log-interval",
type=int,
default=10000,
help="Interval (in timesteps) of printing logs.",
)
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(
"--checkpoint-frequency",
type=int,
default=None,
help="Frequency at which agents are stored.",
)
args = parser.parse_args()
import logging
logging.basicConfig(level=args.log_level)
# Set a random seed used in PFRL.
utils.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.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2**32
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print("Output files are saved in {}".format(args.outdir))
def make_env(idx, test):
# Use different random seeds for train and test envs
process_seed = int(process_seeds[idx])
env_seed = 2**32 - 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,
flicker=args.flicker,
frame_stack=False,
)
env.seed(env_seed)
if args.monitor:
env = pfrl.wrappers.Monitor(
env, args.outdir, mode="evaluation" if test else "training")
if args.render:
env = pfrl.wrappers.Render(env)
return env
def make_batch_env(test):
vec_env = pfrl.envs.MultiprocessVectorEnv([
(lambda: make_env(idx, test))
for idx, env in enumerate(range(args.num_envs))
])
if not args.no_frame_stack:
vec_env = pfrl.wrappers.VectorFrameStack(vec_env, 4)
return vec_env
sample_env = make_batch_env(test=False)
print("Observation space", sample_env.observation_space)
print("Action space", sample_env.action_space)
n_actions = sample_env.action_space.n
obs_n_channels = sample_env.observation_space.low.shape[0]
del sample_env
def lecun_init(layer, gain=1):
if isinstance(layer, (nn.Conv2d, nn.Linear)):
pfrl.initializers.init_lecun_normal(layer.weight, gain)
nn.init.zeros_(layer.bias)
else:
pfrl.initializers.init_lecun_normal(layer.weight_ih_l0, gain)
pfrl.initializers.init_lecun_normal(layer.weight_hh_l0, gain)
nn.init.zeros_(layer.bias_ih_l0)
nn.init.zeros_(layer.bias_hh_l0)
return layer
if args.recurrent:
model = pfrl.nn.RecurrentSequential(
lecun_init(nn.Conv2d(obs_n_channels, 32, 8, stride=4)),
nn.ReLU(),
lecun_init(nn.Conv2d(32, 64, 4, stride=2)),
nn.ReLU(),
lecun_init(nn.Conv2d(64, 64, 3, stride=1)),
nn.ReLU(),
nn.Flatten(),
lecun_init(nn.Linear(3136, 512)),
nn.ReLU(),
lecun_init(nn.GRU(num_layers=1, input_size=512, hidden_size=512)),
pfrl.nn.Branched(
nn.Sequential(
lecun_init(nn.Linear(512, n_actions), 1e-2),
SoftmaxCategoricalHead(),
),
lecun_init(nn.Linear(512, 1)),
),
)
else:
model = nn.Sequential(
lecun_init(nn.Conv2d(obs_n_channels, 32, 8, stride=4)),
nn.ReLU(),
lecun_init(nn.Conv2d(32, 64, 4, stride=2)),
nn.ReLU(),
lecun_init(nn.Conv2d(64, 64, 3, stride=1)),
nn.ReLU(),
nn.Flatten(),
lecun_init(nn.Linear(3136, 512)),
nn.ReLU(),
pfrl.nn.Branched(
nn.Sequential(
lecun_init(nn.Linear(512, n_actions), 1e-2),
SoftmaxCategoricalHead(),
),
lecun_init(nn.Linear(512, 1)),
),
)
opt = torch.optim.Adam(model.parameters(), lr=args.lr, eps=1e-5)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = PPO(
model,
opt,
gpu=args.gpu,
phi=phi,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps=0.1,
clip_eps_vf=None,
standardize_advantages=True,
entropy_coef=1e-2,
recurrent=args.recurrent,
max_grad_norm=0.5,
)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(
env=make_batch_env(test=True),
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:
step_hooks = []
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
for param_group in agent.optimizer.param_groups:
param_group["lr"] = value
step_hooks.append(
experiments.LinearInterpolationHook(args.steps, args.lr, 0,
lr_setter))
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=make_batch_env(False),
eval_env=make_batch_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
checkpoint_freq=args.checkpoint_frequency,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
save_best_so_far_agent=False,
step_hooks=step_hooks,
)