def main():
parser = argparse.ArgumentParser(description="Run an Atari benchmark.")
parser.add_argument("env", help="Name of the Atari game (e.g. Pong).")
parser.add_argument(
"agent",
help="Name of the agent (e.g. dqn). See presets for available agents.")
parser.add_argument(
"--device",
default="cuda",
help=
"The name of the device to run the agent on (e.g. cpu, cuda, cuda:0).",
)
parser.add_argument("--frames",
type=int,
default=40e6,
help="The number of training frames.")
parser.add_argument("--render",
type=bool,
default=False,
help="Render the environment.")
parser.add_argument("--logdir",
default='runs',
help="The base logging directory.")
args = parser.parse_args()
env = AtariEnvironment(args.env, device=args.device)
agent_name = args.agent
agent = getattr(atari, agent_name)
run_experiment(agent(device=args.device, last_frame=args.frames),
env,
args.frames,
render=args.render,
logdir=args.logdir)
def run():
# parse arguments
parser = argparse.ArgumentParser(description="Run an Atari benchmark.")
parser.add_argument("env", help="Name of the Atari game (e.g. Pong)")
parser.add_argument(
"--device",
default="cuda",
help=
"The name of the device to run the agent on (e.g. cpu, cuda, cuda:0)",
)
parser.add_argument("--frames",
type=int,
default=2e6,
help="The number of training frames")
args = parser.parse_args()
# create atari environment
env = AtariEnvironment(args.env, device=args.device)
# run the experiment
run_experiment(model_based_dqn.device(args.device), env, args.frames)
# run the baseline agent for comparison
run_experiment(
dqn.device(args.device).hyperparameters(replay_buffer_size=1e5), env,
args.frames)
def main():
parser = argparse.ArgumentParser(
description="Run a classic control benchmark.")
parser.add_argument("env", help="Name of the env (e.g. CartPole-v1).")
parser.add_argument(
"agent",
help="Name of the agent (e.g. dqn). See presets for available agents.")
parser.add_argument(
"--device",
default="cuda",
help=
"The name of the device to run the agent on (e.g. cpu, cuda, cuda:0).",
)
parser.add_argument("--frames",
type=int,
default=20000,
help="The number of training frames.")
parser.add_argument("--render",
type=bool,
default=False,
help="Render the environment.")
args = parser.parse_args()
env = GymEnvironment(args.env, device=args.device)
agent_name = args.agent
agent = getattr(classic_control, agent_name)
run_experiment(agent(device=args.device),
env,
args.frames,
render=args.render)
def run_procgen():
parser = argparse.ArgumentParser(description="Run an Atari benchmark.")
parser.add_argument("env", help="Name of the procgen env.")
parser.add_argument(
"--device",
default="cuda",
help=
"The name of the device to run the agent on (e.g. cpu, cuda, cuda:0).",
)
parser.add_argument("--frames",
type=int,
default=10e6,
help="The number of training frames.")
parser.add_argument("--render",
type=bool,
default=False,
help="Render the environment.")
args = parser.parse_args()
env = ProcgenAtariEnv(args.env, device='cuda')
run_experiment(a2c(device=args.device, last_frame=args.frames),
env,
args.frames,
render=args.render)
def main():
device = 'cpu'
timesteps = 1e7
env = Atari2Environment('BreakoutNoFrameskip-v4', device=device)
env.seed(2)
run_experiment(
[dqn(device=device)],
[env],
timesteps,
)
def main():
parser = argparse.ArgumentParser(description="Run a continuous actions benchmark.")
parser.add_argument("env", help="Name of the env (e.g. 'lander', 'cheetah')")
parser.add_argument(
"agent", help="Name of the agent (e.g. ddpg). See presets for available agents."
)
parser.add_argument(
"--device",
default="cuda",
help="The name of the device to run the agent on (e.g. cpu, cuda, cuda:0).",
)
parser.add_argument(
"--frames", type=int, default=2e6, help="The number of training frames."
)
parser.add_argument(
"--render", action="store_true", default=False, help="Render the environment."
)
parser.add_argument(
"--logdir", default='runs', help="The base logging directory."
)
parser.add_argument("--writer", default='tensorboard', help="The backend used for tracking experiment metrics.")
parser.add_argument(
'--hyperparameters',
default=[],
nargs='*',
help="Custom hyperparameters, in the format hyperparameter1=value1 hyperparameter2=value2 etc."
)
args = parser.parse_args()
if args.env in ENVS:
env = GymEnvironment(args.env, device=args.device)
elif 'BulletEnv' in args.env or args.env in PybulletEnvironment.short_names:
env = PybulletEnvironment(args.env, device=args.device)
else:
env = GymEnvironment(args.env, device=args.device)
agent_name = args.agent
agent = getattr(continuous, agent_name)
agent = agent.device(args.device)
# parse hyperparameters
hyperparameters = {}
for hp in args.hyperparameters:
key, value = hp.split('=')
hyperparameters[key] = type(agent.default_hyperparameters[key])(value)
agent = agent.hyperparameters(**hyperparameters)
run_experiment(
agent,
env,
frames=args.frames,
render=args.render,
logdir=args.logdir,
writer=args.writer,
)
def main():
device = 'cpu'
timesteps = 40000
run_experiment(
[dqn(), a2c()],
[
GymEnvironment('CartPole-v0', device),
GymEnvironment('Acrobot-v1', device)
],
timesteps,
)
plot_returns_100('runs', timesteps=timesteps)
def main():
parser = argparse.ArgumentParser(description="Run an Atari benchmark.")
parser.add_argument("env", help="Name of the Atari game (e.g. Pong).")
parser.add_argument(
"agent",
help="Name of the agent (e.g. dqn). See presets for available agents.")
parser.add_argument(
"--device",
default="cuda",
help=
"The name of the device to run the agent on (e.g. cpu, cuda, cuda:0).",
)
parser.add_argument("--frames",
type=int,
default=40e6,
help="The number of training frames.")
parser.add_argument("--render",
action="store_true",
default=False,
help="Render the environment.")
parser.add_argument("--logdir",
default='runs',
help="The base logging directory.")
parser.add_argument(
"--writer",
default='tensorboard',
help="The backend used for tracking experiment metrics.")
parser.add_argument('--hyperparameters', default=[], nargs='*')
args = parser.parse_args()
env = AtariEnvironment(args.env, device=args.device)
agent_name = args.agent
agent = getattr(atari, agent_name)
agent = agent.device(args.device)
# parse hyperparameters
hyperparameters = {}
for hp in args.hyperparameters:
key, value = hp.split('=')
hyperparameters[key] = type(agent.default_hyperparameters[key])(value)
agent = agent.hyperparameters(**hyperparameters)
run_experiment(
agent,
env,
args.frames,
render=args.render,
logdir=args.logdir,
writer=args.writer,
)
def main():
parser = argparse.ArgumentParser(
description="Run a continuous actions benchmark.")
parser.add_argument("env", help="Name of the env (see envs)")
parser.add_argument(
"agent",
help="Name of the agent (e.g. ddpg). See presets for available agents."
)
parser.add_argument(
"--device",
default="cuda",
help=
"The name of the device to run the agent on (e.g. cpu, cuda, cuda:0).",
)
parser.add_argument("--frames",
type=int,
default=2e6,
help="The number of training frames.")
parser.add_argument("--render",
type=bool,
default=False,
help="Render the environment.")
parser.add_argument("--logdir",
default='runs',
help="The base logging directory.")
args = parser.parse_args()
if args.env in ENVS:
env_id = ENVS[args.env]
else:
env_id = args.env
env = GymEnvironment(env_id, device=args.device)
agent_name = args.agent
agent = getattr(continuous, agent_name)
run_experiment(agent(device=args.device),
env,
frames=args.frames,
render=args.render,
logdir=args.logdir)
def main():
DEVICE = 'cpu'
# DEVICE = 'cuda' # uncomment for gpu support
timesteps = 40000
run_experiment(
[
# DQN with default hyperparameters
dqn.device(DEVICE),
# DQN with a custom hyperparameters and a custom name.
dqn.device(DEVICE).hyperparameters(replay_buffer_size=100
).name('dqn-small-buffer'),
# A2C with a custom name
a2c.device(DEVICE).name('not-dqn')
],
[
GymEnvironment('CartPole-v0', DEVICE),
GymEnvironment('Acrobot-v1', DEVICE)
],
timesteps,
)
plot_returns_100('runs', timesteps=timesteps)
