#!/usr/bin/env python3 import argparse import datetime import os import pprint import numpy as np import torch from mujoco_env import make_mujoco_env from torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer from tianshou.policy import REDQPolicy from tianshou.trainer import offpolicy_trainer from tianshou.utils import TensorboardLogger, WandbLogger from tianshou.utils.net.common import EnsembleLinear, Net from tianshou.utils.net.continuous import ActorProb, Critic def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="Ant-v3") parser.add_argument("--seed", type=int, default=0) parser.add_argument("--buffer-size", type=int, default=1000000) parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[256, 256]) parser.add_argument("--ensemble-size", type=int, default=10) parser.add_argument("--subset-size", type=int, default=2) parser.add_argument("--actor-lr", type=float, default=1e-3) parser.add_argument("--critic-lr", type=float, default=1e-3) parser.add_argument("--gamma", type=float, default=0.99) parser.add_argument("--tau", type=float, default=0.005) parser.add_argument("--alpha", type=float, default=0.2) parser.add_argument("--auto-alpha", default=False, action="store_true") parser.add_argument("--alpha-lr", type=float, default=3e-4) parser.add_argument("--start-timesteps", type=int, default=10000) parser.add_argument("--epoch", type=int, default=200) 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=20) parser.add_argument("--n-step", type=int, default=1) parser.add_argument("--batch-size", type=int, default=256) parser.add_argument( "--target-mode", type=str, choices=("min", "mean"), default="min" ) 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="mujoco.benchmark") parser.add_argument( "--watch", default=False, action="store_true", help="watch the play of pre-trained policy only", ) return parser.parse_args() def test_redq(args=get_args()): env, train_envs, test_envs = make_mujoco_env( args.task, args.seed, args.training_num, args.test_num, obs_norm=False ) args.state_shape = env.observation_space.shape or env.observation_space.n args.action_shape = env.action_space.shape or env.action_space.n args.max_action = env.action_space.high[0] 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 net_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device) actor = ActorProb( net_a, args.action_shape, max_action=args.max_action, device=args.device, unbounded=True, conditioned_sigma=True, ).to(args.device) actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr) def linear(x, y): return EnsembleLinear(args.ensemble_size, x, y) net_c = Net( args.state_shape, args.action_shape, hidden_sizes=args.hidden_sizes, concat=True, device=args.device, linear_layer=linear, ) critics = Critic( net_c, device=args.device, linear_layer=linear, flatten_input=False, ).to(args.device) critics_optim = torch.optim.Adam(critics.parameters(), lr=args.critic_lr) if args.auto_alpha: target_entropy = -np.prod(env.action_space.shape) log_alpha = torch.zeros(1, requires_grad=True, device=args.device) alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr) args.alpha = (target_entropy, log_alpha, alpha_optim) policy = REDQPolicy( actor, actor_optim, critics, critics_optim, args.ensemble_size, args.subset_size, tau=args.tau, gamma=args.gamma, alpha=args.alpha, estimation_step=args.n_step, actor_delay=args.update_per_step, target_mode=args.target_mode, 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 if args.training_num > 1: buffer = VectorReplayBuffer(args.buffer_size, len(train_envs)) else: buffer = ReplayBuffer(args.buffer_size) 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) # log now = datetime.datetime.now().strftime("%y%m%d-%H%M%S") args.algo_name = "redq" 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")) 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_redq()