128 lines
5.4 KiB
Python
128 lines
5.4 KiB
Python
import os
|
|
import gym
|
|
import torch
|
|
import pprint
|
|
import argparse
|
|
import numpy as np
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.utils import BasicLogger
|
|
from tianshou.env import SubprocVectorEnv
|
|
from tianshou.utils.net.common import Net
|
|
from tianshou.policy import DiscreteSACPolicy
|
|
from tianshou.trainer import offpolicy_trainer
|
|
from tianshou.utils.net.discrete import Actor, Critic
|
|
from tianshou.data import Collector, VectorReplayBuffer
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--task', type=str, default='CartPole-v0')
|
|
parser.add_argument('--seed', type=int, default=1626)
|
|
parser.add_argument('--buffer-size', type=int, default=20000)
|
|
parser.add_argument('--actor-lr', type=float, default=1e-4)
|
|
parser.add_argument('--critic-lr', type=float, default=1e-3)
|
|
parser.add_argument('--alpha-lr', type=float, default=3e-4)
|
|
parser.add_argument('--gamma', type=float, default=0.95)
|
|
parser.add_argument('--tau', type=float, default=0.005)
|
|
parser.add_argument('--alpha', type=float, default=0.05)
|
|
parser.add_argument('--auto-alpha', action="store_true", default=False)
|
|
parser.add_argument('--epoch', type=int, default=5)
|
|
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=128)
|
|
parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[128, 128])
|
|
parser.add_argument('--training-num', type=int, default=10)
|
|
parser.add_argument('--test-num', type=int, default=100)
|
|
parser.add_argument('--logdir', type=str, default='log')
|
|
parser.add_argument('--render', type=float, default=0.0)
|
|
parser.add_argument('--rew-norm', action="store_true", default=False)
|
|
parser.add_argument('--n-step', type=int, default=3)
|
|
parser.add_argument(
|
|
'--device', type=str,
|
|
default='cuda' if torch.cuda.is_available() else 'cpu')
|
|
args = parser.parse_known_args()[0]
|
|
return args
|
|
|
|
|
|
def test_discrete_sac(args=get_args()):
|
|
env = gym.make(args.task)
|
|
args.state_shape = env.observation_space.shape or env.observation_space.n
|
|
args.action_shape = env.action_space.shape or env.action_space.n
|
|
|
|
train_envs = SubprocVectorEnv(
|
|
[lambda: gym.make(args.task) for _ in range(args.training_num)])
|
|
test_envs = SubprocVectorEnv(
|
|
[lambda: gym.make(args.task) for _ in range(args.test_num)])
|
|
# seed
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
train_envs.seed(args.seed)
|
|
test_envs.seed(args.seed)
|
|
# model
|
|
net = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
|
|
device=args.device)
|
|
actor = Actor(net, args.action_shape,
|
|
softmax_output=False, device=args.device).to(args.device)
|
|
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
|
net_c1 = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
|
|
device=args.device)
|
|
critic1 = Critic(net_c1, last_size=args.action_shape,
|
|
device=args.device).to(args.device)
|
|
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
|
|
net_c2 = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
|
|
device=args.device)
|
|
critic2 = Critic(net_c2, last_size=args.action_shape,
|
|
device=args.device).to(args.device)
|
|
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
|
|
|
|
# better not to use auto alpha in CartPole
|
|
if args.auto_alpha:
|
|
target_entropy = 0.98 * np.log(np.prod(args.action_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 = DiscreteSACPolicy(
|
|
actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
|
|
args.tau, args.gamma, args.alpha, estimation_step=args.n_step,
|
|
reward_normalization=args.rew_norm)
|
|
# collector
|
|
train_collector = Collector(
|
|
policy, train_envs,
|
|
VectorReplayBuffer(args.buffer_size, len(train_envs)))
|
|
test_collector = Collector(policy, test_envs)
|
|
# train_collector.collect(n_step=args.buffer_size)
|
|
# log
|
|
log_path = os.path.join(args.logdir, args.task, 'discrete_sac')
|
|
writer = SummaryWriter(log_path)
|
|
logger = BasicLogger(writer)
|
|
|
|
def save_fn(policy):
|
|
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
|
|
|
|
def stop_fn(mean_rewards):
|
|
return mean_rewards >= env.spec.reward_threshold
|
|
|
|
# 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, stop_fn=stop_fn, save_fn=save_fn, logger=logger,
|
|
update_per_step=args.update_per_step, test_in_train=False)
|
|
assert stop_fn(result['best_reward'])
|
|
if __name__ == '__main__':
|
|
pprint.pprint(result)
|
|
# Let's watch its performance!
|
|
env = gym.make(args.task)
|
|
policy.eval()
|
|
collector = Collector(policy, env)
|
|
result = collector.collect(n_episode=1, render=args.render)
|
|
rews, lens = result["rews"], result["lens"]
|
|
print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_discrete_sac()
|