#!/usr/bin/env python3 # isort: skip_file import argparse import datetime import os import pprint import gym import numpy as np import torch import wandb from torch.utils.tensorboard import SummaryWriter from tianshou.data import ( Collector, HERReplayBuffer, HERVectorReplayBuffer, ReplayBuffer, VectorReplayBuffer, ) from tianshou.env import ShmemVectorEnv, TruncatedAsTerminated from tianshou.exploration import GaussianNoise from tianshou.policy import DDPGPolicy from tianshou.trainer import offpolicy_trainer from tianshou.utils import TensorboardLogger, WandbLogger from tianshou.utils.net.common import Net, get_dict_state_decorator from tianshou.utils.net.continuous import Actor, Critic def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="FetchReach-v3") parser.add_argument("--seed", type=int, default=0) parser.add_argument("--buffer-size", type=int, default=100000) parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[256, 256]) parser.add_argument("--actor-lr", type=float, default=1e-3) parser.add_argument("--critic-lr", type=float, default=3e-3) parser.add_argument("--gamma", type=float, default=0.99) parser.add_argument("--tau", type=float, default=0.005) parser.add_argument("--exploration-noise", type=float, default=0.1) parser.add_argument("--start-timesteps", type=int, default=25000) parser.add_argument("--epoch", type=int, default=10) parser.add_argument("--step-per-epoch", type=int, default=5000) parser.add_argument("--step-per-collect", type=int, default=1) parser.add_argument("--update-per-step", type=int, default=1) parser.add_argument("--n-step", type=int, default=1) parser.add_argument("--batch-size", type=int, default=512) parser.add_argument( "--replay-buffer", type=str, default="her", choices=["normal", "her"] ) parser.add_argument("--her-horizon", type=int, default=50) parser.add_argument("--her-future-k", type=int, default=8) parser.add_argument("--training-num", type=int, default=1) 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("--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="HER-benchmark") parser.add_argument( "--watch", default=False, action="store_true", help="watch the play of pre-trained policy only", ) return parser.parse_args() def make_fetch_env(task, training_num, test_num): env = TruncatedAsTerminated(gym.make(task)) train_envs = ShmemVectorEnv( [lambda: TruncatedAsTerminated(gym.make(task)) for _ in range(training_num)] ) test_envs = ShmemVectorEnv( [lambda: TruncatedAsTerminated(gym.make(task)) for _ in range(test_num)] ) return env, train_envs, test_envs def test_ddpg(args=get_args()): # log now = datetime.datetime.now().strftime("%y%m%d-%H%M%S") args.algo_name = "ddpg" 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, ) logger.wandb_run.config.setdefaults(vars(args)) args = argparse.Namespace(**wandb.config) writer = SummaryWriter(log_path) writer.add_text("args", str(args)) if args.logger == "tensorboard": logger = TensorboardLogger(writer) else: # wandb logger.load(writer) env, train_envs, test_envs = make_fetch_env( args.task, args.training_num, args.test_num ) args.state_shape = { 'observation': env.observation_space['observation'].shape, 'achieved_goal': env.observation_space['achieved_goal'].shape, 'desired_goal': env.observation_space['desired_goal'].shape, } args.action_shape = env.action_space.shape or env.action_space.n args.max_action = env.action_space.high[0] args.exploration_noise = args.exploration_noise * args.max_action print("Observations shape:", args.state_shape) print("Actions shape:", args.action_shape) print("Action range:", np.min(env.action_space.low), np.max(env.action_space.high)) # seed np.random.seed(args.seed) torch.manual_seed(args.seed) # model dict_state_dec, flat_state_shape = get_dict_state_decorator( state_shape=args.state_shape, keys=['observation', 'achieved_goal', 'desired_goal'] ) net_a = dict_state_dec(Net)( flat_state_shape, hidden_sizes=args.hidden_sizes, device=args.device ) actor = dict_state_dec(Actor)( net_a, args.action_shape, max_action=args.max_action, device=args.device ).to(args.device) actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr) net_c = dict_state_dec(Net)( flat_state_shape, action_shape=args.action_shape, hidden_sizes=args.hidden_sizes, concat=True, device=args.device, ) critic = dict_state_dec(Critic)(net_c, device=args.device).to(args.device) critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr) policy = DDPGPolicy( actor, actor_optim, critic, critic_optim, tau=args.tau, gamma=args.gamma, exploration_noise=GaussianNoise(sigma=args.exploration_noise), estimation_step=args.n_step, action_space=env.action_space, ) # 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) # collector def compute_reward_fn(ag: np.ndarray, g: np.ndarray): return env.compute_reward(ag, g, {}) if args.replay_buffer == "normal": if args.training_num > 1: buffer = VectorReplayBuffer(args.buffer_size, len(train_envs)) else: buffer = ReplayBuffer(args.buffer_size) else: if args.training_num > 1: buffer = HERVectorReplayBuffer( args.buffer_size, len(train_envs), compute_reward_fn=compute_reward_fn, horizon=args.her_horizon, future_k=args.her_future_k, ) else: buffer = HERReplayBuffer( args.buffer_size, compute_reward_fn=compute_reward_fn, horizon=args.her_horizon, future_k=args.her_future_k, ) train_collector = Collector(policy, train_envs, buffer, exploration_noise=True) test_collector = Collector(policy, test_envs) train_collector.collect(n_step=args.start_timesteps, random=True) def save_best_fn(policy): torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth")) if not args.watch: # 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, save_best_fn=save_best_fn, logger=logger, update_per_step=args.update_per_step, test_in_train=False, ) pprint.pprint(result) # Let's watch its performance! policy.eval() test_envs.seed(args.seed) test_collector.reset() result = test_collector.collect(n_episode=args.test_num, render=args.render) print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}') if __name__ == "__main__": test_ddpg()