1423eeb3b2
- Fix the current bug discussed in #844 in `test_ppo.py`. - Add warning for `ActorProb ` if both `max_action ` and `unbounded=True` are used for model initializations. - Add warning for PGpolicy and DDPGpolicy if they find duplicate usage of action-bounded actor and action scaling method.
230 lines
7.9 KiB
Python
230 lines
7.9 KiB
Python
import argparse
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import datetime
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import os
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import pickle
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import pprint
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import gymnasium as gym
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import numpy as np
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.env import DummyVectorEnv
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from tianshou.policy import CQLPolicy
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from tianshou.trainer import OfflineTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import Net
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from tianshou.utils.net.continuous import ActorProb, Critic
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if __name__ == "__main__":
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from gather_pendulum_data import expert_file_name, gather_data
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else: # pytest
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from test.offline.gather_pendulum_data import expert_file_name, gather_data
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--task', type=str, default='Pendulum-v1')
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parser.add_argument('--reward-threshold', type=float, default=None)
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
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parser.add_argument('--actor-lr', type=float, default=1e-3)
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parser.add_argument('--critic-lr', type=float, default=1e-3)
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parser.add_argument('--alpha', type=float, default=0.2)
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parser.add_argument('--auto-alpha', default=True, action='store_true')
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parser.add_argument('--alpha-lr', type=float, default=1e-3)
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parser.add_argument('--cql-alpha-lr', type=float, default=1e-3)
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parser.add_argument("--start-timesteps", type=int, default=10000)
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parser.add_argument('--epoch', type=int, default=5)
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parser.add_argument('--step-per-epoch', type=int, default=500)
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parser.add_argument('--n-step', type=int, default=3)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument("--tau", type=float, default=0.005)
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parser.add_argument("--temperature", type=float, default=1.0)
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parser.add_argument("--cql-weight", type=float, default=1.0)
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parser.add_argument("--with-lagrange", type=bool, default=True)
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parser.add_argument("--lagrange-threshold", type=float, default=10.0)
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parser.add_argument("--gamma", type=float, default=0.99)
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parser.add_argument("--eval-freq", type=int, default=1)
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parser.add_argument('--test-num', type=int, default=10)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=1 / 35)
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parser.add_argument(
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'--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
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)
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parser.add_argument('--resume-path', type=str, default=None)
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parser.add_argument(
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'--watch',
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default=False,
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action='store_true',
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help='watch the play of pre-trained policy only',
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)
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parser.add_argument("--load-buffer-name", type=str, default=expert_file_name())
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args = parser.parse_known_args()[0]
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return args
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def test_cql(args=get_args()):
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if os.path.exists(args.load_buffer_name) and os.path.isfile(args.load_buffer_name):
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if args.load_buffer_name.endswith(".hdf5"):
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buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name)
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else:
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buffer = pickle.load(open(args.load_buffer_name, "rb"))
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else:
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buffer = gather_data()
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env = gym.make(args.task)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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args.max_action = env.action_space.high[0] # float
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if args.reward_threshold is None:
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# too low?
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default_reward_threshold = {"Pendulum-v0": -1200, "Pendulum-v1": -1200}
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args.reward_threshold = default_reward_threshold.get(
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args.task, env.spec.reward_threshold
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)
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args.state_dim = args.state_shape[0]
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args.action_dim = args.action_shape[0]
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# test_envs = gym.make(args.task)
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test_envs = DummyVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.test_num)]
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)
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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test_envs.seed(args.seed)
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# model
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# actor network
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net_a = Net(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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)
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actor = ActorProb(
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net_a,
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action_shape=args.action_shape,
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device=args.device,
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unbounded=True,
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conditioned_sigma=True,
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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# critic network
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net_c1 = Net(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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)
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net_c2 = Net(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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)
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critic1 = Critic(net_c1, device=args.device).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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critic2 = Critic(net_c2, device=args.device).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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if args.auto_alpha:
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target_entropy = -np.prod(env.action_space.shape)
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log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
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alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
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args.alpha = (target_entropy, log_alpha, alpha_optim)
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policy = CQLPolicy(
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actor,
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actor_optim,
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critic1,
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critic1_optim,
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critic2,
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critic2_optim,
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cql_alpha_lr=args.cql_alpha_lr,
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cql_weight=args.cql_weight,
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tau=args.tau,
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gamma=args.gamma,
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alpha=args.alpha,
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temperature=args.temperature,
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with_lagrange=args.with_lagrange,
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lagrange_threshold=args.lagrange_threshold,
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min_action=np.min(env.action_space.low),
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max_action=np.max(env.action_space.high),
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device=args.device,
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)
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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
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print("Loaded agent from: ", args.resume_path)
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# collector
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# buffer has been gathered
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# train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
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test_collector = Collector(policy, test_envs)
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# log
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t0 = datetime.datetime.now().strftime("%m%d_%H%M%S")
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log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_cql'
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log_path = os.path.join(args.logdir, args.task, 'cql', log_file)
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger = TensorboardLogger(writer)
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def save_best_fn(policy):
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torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
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def stop_fn(mean_rewards):
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return mean_rewards >= args.reward_threshold
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def watch():
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policy.load_state_dict(
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torch.load(
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os.path.join(log_path, 'policy.pth'), map_location=torch.device('cpu')
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)
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)
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policy.eval()
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collector = Collector(policy, env)
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collector.collect(n_episode=1, render=1 / 35)
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# trainer
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trainer = OfflineTrainer(
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policy,
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buffer,
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test_collector,
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args.epoch,
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args.step_per_epoch,
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args.test_num,
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args.batch_size,
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save_best_fn=save_best_fn,
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stop_fn=stop_fn,
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logger=logger,
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)
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for epoch, epoch_stat, info in trainer:
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print(f"Epoch: {epoch}")
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print(epoch_stat)
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print(info)
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assert stop_fn(info["best_reward"])
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# Let's watch its performance!
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if __name__ == "__main__":
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pprint.pprint(info)
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env = gym.make(args.task)
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policy.eval()
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collector = Collector(policy, env)
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result = collector.collect(n_episode=1, render=args.render)
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rews, lens = result["rews"], result["lens"]
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print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
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if __name__ == '__main__':
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test_cql()
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