necsa_dqn.py

import argparse
import datetime
import os, json
import pprint

import numpy as np
import torch
from atari_network import DQN
from atari_wrapper import make_atari_env
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer, NECSA_Atari_Collector
from tianshou.policy import DQNPolicy
from tianshou.policy.modelbased.icm import ICMPolicy
from tianshou.trainer import offpolicy_trainer
from tianshou.utils import TensorboardLogger, WandbLogger
from tianshou.utils.net.discrete import IntrinsicCuriosityModule


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="PongNoFrameskip-v4")
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--scale-obs", type=int, default=0)
    parser.add_argument("--eps-test", type=float, default=0.005)
    parser.add_argument("--eps-train", type=float, default=1.)
    parser.add_argument("--eps-train-final", type=float, default=0.05)
    parser.add_argument("--buffer-size", type=int, default=100000)
    parser.add_argument("--lr", type=float, default=0.0001)
    parser.add_argument("--gamma", type=float, default=0.99)
    parser.add_argument("--n-step", type=int, default=3)
    parser.add_argument("--target-update-freq", type=int, default=500)
    parser.add_argument("--epoch", type=int, default=1000)
    parser.add_argument("--step-per-epoch", type=int, default=10000)
    parser.add_argument("--step-per-collect", type=int, default=10)
    parser.add_argument("--update-per-step", type=float, default=0.1)
    parser.add_argument("--batch-size", type=int, default=32)
    parser.add_argument("--training-num", type=int, default=10)
    parser.add_argument("--test-num", type=int, default=10)
    parser.add_argument("--logdir", type=str, default="log")
    parser.add_argument("--render", type=float, default=0.)
    parser.add_argument(
        "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu"
    )
    parser.add_argument("--frames-stack", type=int, default=4)
    parser.add_argument("--resume-path", type=str, default=None)
    parser.add_argument("--resume-id", type=str, default=None)
    parser.add_argument(
        "--logger",
        type=str,
        default="tensorboard",
        choices=["tensorboard", "wandb"],
    )
    parser.add_argument("--wandb-project", type=str, default="atari.benchmark")
    parser.add_argument(
        "--watch",
        default=False,
        action="store_true",
        help="watch the play of pre-trained policy only"
    )
    parser.add_argument("--save-buffer-name", type=str, default=None)
    parser.add_argument(
        "--icm-lr-scale",
        type=float,
        default=0.,
        help="use intrinsic curiosity module with this lr scale"
    )
    parser.add_argument(
        "--icm-reward-scale",
        type=float,
        default=0.01,
        help="scaling factor for intrinsic curiosity reward"
    )
    parser.add_argument(
        "--icm-forward-loss-weight",
        type=float,
        default=0.2,
        help="weight for the forward model loss in ICM"
    )


    parser.add_argument("--step", type=int, default=1)                  # Directory for storing all experimental data
    parser.add_argument("--grid_num", type=int, default=5)              # Directory for storing all experimental data
    parser.add_argument("--epsilon", type=float, default=0.1)            # Directory for storing all experimental data
    parser.add_argument("--raw_state_dim", type=int, default=64) 
    parser.add_argument("--state_dim", type=int, default=24) 
    parser.add_argument("--state_min", type=float, default=0 )        # 
    parser.add_argument("--state_max", type=float, default=1 )         # state_max, state_min
    parser.add_argument("--mode", type=str, default='hidden', choices=['state', 'state_action', 'hidden'] )   # 
    parser.add_argument("--reduction", action="store_true")   # 

    return parser.parse_args()


def test_dqn(args=get_args()):
    env, train_envs, test_envs = make_atari_env(
        args.task,
        args.seed,
        args.training_num,
        args.test_num,
        scale=args.scale_obs,
        frame_stack=args.frames_stack,
    )
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    # should be N_FRAMES x H x W
    print("Observations shape:", args.state_shape)
    print("Actions shape:", args.action_shape)
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    # define model
    net = DQN(*args.state_shape, args.action_shape, args.device).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    # define policy
    policy = DQNPolicy(
        net,
        optim,
        args.gamma,
        args.n_step,
        target_update_freq=args.target_update_freq
    )
    if args.icm_lr_scale > 0:
        feature_net = DQN(
            *args.state_shape, args.action_shape, args.device, features_only=True
        )
        action_dim = np.prod(args.action_shape)
        feature_dim = feature_net.output_dim
        icm_net = IntrinsicCuriosityModule(
            feature_net.net,
            feature_dim,
            action_dim,
            hidden_sizes=[512],
            device=args.device
        )
        icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr)
        policy = ICMPolicy(
            policy, icm_net, icm_optim, args.icm_lr_scale, args.icm_reward_scale,
            args.icm_forward_loss_weight
        ).to(args.device)
    # load a previous policy
    if args.resume_path:
        policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
        print("Loaded agent from: ", args.resume_path)
    # replay buffer: `save_last_obs` and `stack_num` can be removed together
    # when you have enough RAM
    buffer = VectorReplayBuffer(
        args.buffer_size,
        buffer_num=len(train_envs),
        ignore_obs_next=True,
        save_only_last_obs=True,
        stack_num=args.frames_stack
    )

    if not args.reduction:
        args.state_dim = args.raw_state_dim

    NECSA_DICT = {
        'step' : args.step,
        'grid_num' : args.grid_num,
        'epsilon' : args.epsilon,
        'mode' : args.mode,
        'reduction' : args.reduction,
        'raw_state_dim' : args.raw_state_dim,
        'state_dim' : args.state_dim,
        'state_min' : args.state_min,
        'state_max' : args.state_max,
        'action_dim' : None,
        'action_min' : None,
        'action_max' : None
    }
    print(NECSA_DICT)
    
    args.algo_name = "necsa_dqn"
    # collector
    train_collector = NECSA_Atari_Collector(policy, train_envs, buffer, exploration_noise=True, NECSA_DICT = NECSA_DICT)
    test_collector = Collector(policy, test_envs, exploration_noise=True)

    # log
    now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
    #args.algo_name = "dqn_icm" if args.icm_lr_scale > 0 else "dqn"
    log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
    log_path = os.path.join(args.logdir, log_name)

    # logger
    if args.logger == "wandb":
        logger = WandbLogger(
            save_interval=1,
            name=log_name.replace(os.path.sep, "__"),
            run_id=args.resume_id,
            config=args,
            project=args.wandb_project,
        )
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    if args.logger == "tensorboard":
        logger = TensorboardLogger(writer)
    else:  # wandb
        logger.load(writer)

    def save_best_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

    def stop_fn(mean_rewards):
        if env.spec.reward_threshold:
            return mean_rewards >= env.spec.reward_threshold
        elif "Pong" in args.task:
            return mean_rewards >= 20
        else:
            return False

    def train_fn(epoch, env_step):
        # nature DQN setting, linear decay in the first 1M steps
        if env_step <= 1e6:
            eps = args.eps_train - env_step / 1e6 * \
                (args.eps_train - args.eps_train_final)
        else:
            eps = args.eps_train_final
        policy.set_eps(eps)
        if env_step % 1000 == 0:
            logger.write("train/env_step", env_step, {"train/eps": eps})

    def test_fn(epoch, env_step):
        policy.set_eps(args.eps_test)

    def save_checkpoint_fn(epoch, env_step, gradient_step):
        # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
        ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
        torch.save({"model": policy.state_dict()}, ckpt_path)
        return ckpt_path

    # watch agent's performance
    def watch():
        print("Setup test envs ...")
        policy.eval()
        policy.set_eps(args.eps_test)
        test_envs.seed(args.seed)
        if args.save_buffer_name:
            print(f"Generate buffer with size {args.buffer_size}")
            buffer = VectorReplayBuffer(
                args.buffer_size,
                buffer_num=len(test_envs),
                ignore_obs_next=True,
                save_only_last_obs=True,
                stack_num=args.frames_stack
            )
            collector = Collector(policy, test_envs, buffer, exploration_noise=True)
            result = collector.collect(n_step=args.buffer_size)
            print(f"Save buffer into {args.save_buffer_name}")
            # Unfortunately, pickle will cause oom with 1M buffer size
            buffer.save_hdf5(args.save_buffer_name)
        else:
            print("Testing agent ...")
            test_collector.reset()
            result = test_collector.collect(
                n_episode=args.test_num, render=args.render
            )
        rew = result["rews"].mean()
        print(f"Mean reward (over {result['n/ep']} episodes): {rew}")

    if args.watch:
        watch()
        exit(0)

    # test train_collector and start filling replay buffer
    train_collector.collect(n_step=args.batch_size * args.training_num)
    # trainer
    result = offpolicy_trainer(
        policy,
        train_collector,
        test_collector,
        args.epoch,
        args.step_per_epoch,
        args.step_per_collect,
        args.test_num,
        args.batch_size,
        train_fn=train_fn,
        test_fn=test_fn,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        logger=logger,
        update_per_step=args.update_per_step,
        test_in_train=False,
        resume_from_log=args.resume_id is not None,
        save_checkpoint_fn=save_checkpoint_fn,
    )

    pprint.pprint(result)
    watch()

    reward_save_path = 'results/' + args.task + '/' + args.algo_name.upper()
    if 'necsa' in args.algo_name:
        reward_save_path = reward_save_path + '_' + str(args.step)
    now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
    os.makedirs(reward_save_path, exist_ok=True)
    reward_save_path = reward_save_path + '/' + now + '.json'
    print(reward_save_path)
    with open(reward_save_path, 'w') as f:
        json.dump(test_collector.policy_eval_results, f)

if __name__ == "__main__":
    test_dqn(get_args())