main.py

import os
import gym
import torch
import argparse
import numpy as np
from collections import deque

import agent
import replay_buffer

import warnings

warnings.filterwarnings("ignore")

if torch.cuda.is_available():
    torch.backends.cuda.matmul.allow_tf32 = True
    torch.set_default_tensor_type("torch.cuda.FloatTensor")

# Runs policy for X episodes and returns average reward
# A fixed seed is used for the eval environment
def eval_policy(policy, env_name, seed, eval_episodes=10):
    eval_env = gym.make(env_name)
    eval_env.seed(seed + 100)

    avg_reward = 0.0
    for _ in range(eval_episodes):
        state, done = eval_env.reset(), False
        while not done:
            action = policy.select_action(np.array(state))
            state, reward, done, _ = eval_env.step(action)
            avg_reward += reward

    avg_reward /= eval_episodes

    print("---------------------------------------")
    print(f"Evaluation over {eval_episodes} episodes: {avg_reward:.3f}")
    print("---------------------------------------")
    return avg_reward


if __name__ == "__main__":

    parser = argparse.ArgumentParser()
    parser.add_argument("--policy", default="TD3")  # Policy name (TD3, DDPG or OurDDPG)
    parser.add_argument("--env", default="Pendulum-v1")  # OpenAI gym environment name
    parser.add_argument(
        "--seed", default=0, type=int
    )  # Sets Gym, PyTorch and Numpy seeds
    parser.add_argument(
        "--start_timesteps", default=1e3, type=int
    )  # Time steps initial random policy is used
    parser.add_argument(
        "--eval_freq", default=5e3, type=int
    )  # How often (time steps) we evaluate
    parser.add_argument(
        "--max_timesteps", default=1e6, type=int
    )  # Max time steps to run environment
    parser.add_argument(
        "--expl_noise", default=0.1
    )  # Std of Gaussian exploration noise
    parser.add_argument(
        "--batch_size", default=256, type=int
    )  # Batch size for both actor and critic
    parser.add_argument("--discount", default=0.99)  # Discount factor
    parser.add_argument("--tau", default=0.005)  # Target network update rate
    parser.add_argument(
        "--policy_noise", default=0.2
    )  # Noise added to target policy during critic update
    parser.add_argument(
        "--noise_clip", default=0.5
    )  # Range to clip target policy noise
    parser.add_argument(
        "--policy_freq", default=2, type=int
    )  # Frequency of delayed policy updates
    save_model = False  # Save model and optimizer parameters
    parser.add_argument(
        "--load_model", default=""
    )  # Model load file name, "" doesn't load, "default" uses file_name
    args = parser.parse_args()

    file_name = f"{args.policy}_{args.env}_{args.seed}"
    print("---------------------------------------")
    print(f"Policy: {args.policy}, Env: {args.env}, Seed: {args.seed}")
    print("---------------------------------------")

    if not os.path.exists("./results"):
        os.makedirs("./results")

    if save_model and not os.path.exists("./models"):
        os.makedirs("./models")

    env = gym.make(args.env)

    # Set seeds
    env.seed(args.seed)
    env.action_space.seed(args.seed)
    torch.manual_seed(args.seed)
    np.random.seed(args.seed)

    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.shape[0]
    max_action = float(env.action_space.high[0])
    his_len = 5

    kwargs = {
        "state_dim": state_dim,
        "action_dim": action_dim,
        "max_action": max_action,
        "discount": args.discount,
        "tau": args.tau,
    }

    # Initialize policy
    if args.policy == "TD3":
        # Target policy smoothing is scaled wrt the action scale
        kwargs["policy_noise"] = args.policy_noise * max_action
        kwargs["noise_clip"] = args.noise_clip * max_action
        kwargs["policy_freq"] = args.policy_freq
        policy = agent.TD3(**kwargs)

    if args.load_model != "":
        policy_file = file_name if args.load_model == "default" else args.load_model
        policy.load(f"./models/{policy_file}")

    replay_buffer = replay_buffer.ReplayBuffer(state_dim, action_dim)

    # Evaluate untrained policy
    # evaluations = [eval_policy(policy, args.env, args.seed)]

    state, done = env.reset(), False
    episode_reward = 0
    episode_timesteps = 0
    episode_num = 0
    obs_buffer = deque([np.zeros([1, state_dim])] * his_len, maxlen=his_len)

    for t in range(int(args.max_timesteps)):

        episode_timesteps += 1

        obs_buffer.append([state])
        assert np.array(obs_buffer).shape == (his_len, 1, state_dim)
        buffer = np.array(obs_buffer).reshape(1, his_len, state_dim)

        # Select action randomly or according to policy
        if t < args.start_timesteps:
            action = env.action_space.sample()
        else:
            action = (
                policy.select_action(buffer)
                + np.random.normal(0, max_action * args.expl_noise, size=action_dim)
            ).clip(-max_action, max_action)

        # Perform action
        next_state, reward, done, _ = env.step(action)
        done_bool = float(done) if episode_timesteps < env._max_episode_steps else 0

        # Store data in replay buffer
        replay_buffer.add(state, action, next_state, reward, done_bool)

        state = next_state
        episode_reward += reward

        # Train agent after collecting sufficient data
        if t >= args.start_timesteps:
            policy.train(replay_buffer, args.batch_size)

        if done:
            # +1 to account for 0 indexing. +0 on ep_timesteps since it will increment +1 even if done=True
            print(
                f"Total T: {t+1} Episode Num: {episode_num+1} Episode T: {episode_timesteps} Reward: {episode_reward:.3f}"
            )
            # Reset environment
            state, done = env.reset(), False
            episode_reward = 0
            episode_timesteps = 0
            episode_num += 1
            obs_buffer = deque([np.zeros([1, state_dim])] * his_len, maxlen=his_len)

        # Evaluate episode
        # if (t + 1) % args.eval_freq == 0:
        #     evaluations.append(eval_policy(policy, args.env, args.seed))
        #     np.save(f"./results/{file_name}", evaluations)
        #     if save_model:
        #         policy.save(f"./models/{file_name}")