sac.py

"""
The script to run SAC on continuous control environments.
"""

import argparse

from RLAlgos.SAC import SAC

from Networks.ActorNetworks import SACActor
from Networks.QValueNetworks import QNetworkContinuousControl

from utils.env_makers import classic_control_env_maker


def parse_args():
    parser = argparse.ArgumentParser(description="Run SAC on continuous control environments.")

    parser.add_argument("--exp-name", type=str, default="sac")

    parser.add_argument("--env-id", type=str, default="Ant-v4")
    parser.add_argument("--render", type=bool, default=False)

    parser.add_argument("--seed", type=int, default=1)
    parser.add_argument("--cuda", type=int, default=0)
    parser.add_argument("--gamma", type=float, default=0.99)

    parser.add_argument("--buffer-size", type=int, default=1000000)
    parser.add_argument("--rb-optimize-memory", type=bool, default=False)
    parser.add_argument("--batch-size", type=int, default=256)

    parser.add_argument("--policy-lr", type=float, default=3e-4)
    parser.add_argument("--q-lr", type=float, default=1e-3)
    parser.add_argument("--alpha-lr", type=float, default=1e-4)

    parser.add_argument("--target-network-frequency", type=int, default=1)
    parser.add_argument("--tau", type=float, default=0.005)

    parser.add_argument("--policy-frequency", type=int, default=2)

    parser.add_argument("--alpha", type=float, default=0.2)
    parser.add_argument("--alpha-autotune", type=bool, default=True)

    parser.add_argument("--write-frequency", type=int, default=100)
    parser.add_argument("--save-folder", type=str, default="./sac/")

    parser.add_argument("--total-timesteps", type=int, default=1000000)
    parser.add_argument("--learning-starts", type=int, default=5e3)

    args = parser.parse_args()
    return args


def run():
    args = parse_args()

    env = classic_control_env_maker(env_id=args.env_id, seed=args.seed, render=args.render)

    agent = SAC(env=env, actor_class=SACActor, critic_class=QNetworkContinuousControl, exp_name=args.exp_name,
                seed=args.seed, cuda=args.cuda, gamma=args.gamma, buffer_size=args.buffer_size,
                rb_optimize_memory=args.rb_optimize_memory, batch_size=args.batch_size, policy_lr=args.policy_lr,
                q_lr=args.q_lr, alpha_lr=args.alpha_lr, target_network_frequency=args.target_network_frequency,
                tau=args.tau, policy_frequency=args.policy_frequency, alpha=args.alpha,
                alpha_autotune=args.alpha_autotune, write_frequency=args.write_frequency, save_folder=args.save_folder)

    agent.learn(total_timesteps=args.total_timesteps, learning_starts=args.learning_starts)

    agent.save(indicator="final")


if __name__ == "__main__":
    run()