def run():
parser = argparse.ArgumentParser(
description="Run a RL agent on an AI gym environment")
parser.add_argument(
"--env-name",
default=constants.DEFAULT_ENV_NAME,
help="The gym environment to run",
)
parser.add_argument(
"--algorithm",
default=constants.DEFAULT_ALGORITHM,
type=str,
choices=["random", "dqn", "a3c", "ppo"],
help="The algorihtm to use for the RL agent.",
)
parser.add_argument("--train",
dest="train",
action="store_true",
help="Train our model.")
parser.add_argument(
"--lr",
default=constants.DEFAULT_LEARNING_RATE,
help="Learning rate for the shared optimizer.",
)
parser.add_argument(
"--update-freq",
default=constants.DEFAULT_UPDATE_FREQUENCY,
type=int,
help="How often to update the global model.",
)
parser.add_argument(
"--max-episodes",
default=constants.DEFAULT_MAX_EPISODES,
type=int,
help="Global maximum number of episodes to run.",
)
parser.add_argument("--gamma",
default=constants.DEFAULT_GAMMA,
help="Discount factor of rewards.")
parser.add_argument(
"--save-dir", help="Directory in which you desire to save the model.")
parser.add_argument("--log-level", default="DEBUG")
parser.add_argument("--thread-count",
type=int,
default=multiprocessing.cpu_count())
args = parser.parse_args()
logging.basicConfig(level=args.log_level)
if args.algorithm == "random":
agent = RandomAgent(args.env_name, args.max_episodes)
elif args.algorithm == "dqn":
agent = DQNAgent(args.env_name,
max_episodes=args.max_episodes,
save_dir=args.save_dir)
elif args.algorithm == "a3c":
agent = A3CAgent(
env_name=args.env_name,
learning_rate=args.lr,
max_episodes=args.max_episodes,
save_dir=args.save_dir,
)
elif args.algorithm == "ppo":
agent = PPOAgent(
env_name=args.env_name,
save_dir=args.save_dir,
max_episodes=args.max_episodes,
)
if args.train:
agent.train()
else:
if hasattr(agent, "model"):
agent.model = agent.load_model(agent.model)
play.play(agent, args.env_name, args.max_episodes)
def test_play_discrete_random():
agent = RandomAgent(discrete_env_name, 1)
play.play(agent, discrete_env_name, max_steps=1, render=False)
def test_play_continuous_a3c():
agent = A3CAgent(continuous_env_name)
play.play(agent, continuous_env_name, max_steps=1, render=False)
def test_play_continuous_random():
agent = RandomAgent(continuous_env_name, 1)
play.play(agent, continuous_env_name, max_steps=1, render=False)
def test_play_discrete_dqn():
agent = DQNAgent(discrete_env_name)
play.play(agent, discrete_env_name, max_steps=1, render=False)
def test_play_discrete_a3c():
agent = A3CAgent(discrete_env_name)
play.play(agent, discrete_env_name, max_steps=1, render=False)