200 lines
6.6 KiB
Python
200 lines
6.6 KiB
Python
import argparse
|
|
import os
|
|
import pprint
|
|
|
|
import gym
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.data import Collector, VectorReplayBuffer
|
|
from tianshou.env import SubprocVectorEnv
|
|
from tianshou.policy import SACPolicy
|
|
from tianshou.trainer import offpolicy_trainer
|
|
from tianshou.utils import TensorboardLogger
|
|
from tianshou.utils.net.common import Net
|
|
from tianshou.utils.net.continuous import ActorProb, Critic
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--task', type=str, default="BipedalWalkerHardcore-v3")
|
|
parser.add_argument('--seed', type=int, default=0)
|
|
parser.add_argument('--buffer-size', type=int, default=1000000)
|
|
parser.add_argument('--actor-lr', type=float, default=3e-4)
|
|
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=0.1)
|
|
parser.add_argument('--auto-alpha', type=int, default=1)
|
|
parser.add_argument('--alpha-lr', type=float, default=3e-4)
|
|
parser.add_argument('--epoch', type=int, default=100)
|
|
parser.add_argument('--step-per-epoch', type=int, default=100000)
|
|
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.)
|
|
parser.add_argument('--n-step', type=int, default=4)
|
|
parser.add_argument(
|
|
'--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
|
|
)
|
|
parser.add_argument('--resume-path', type=str, default=None)
|
|
return parser.parse_args()
|
|
|
|
|
|
class Wrapper(gym.Wrapper):
|
|
"""Env wrapper for reward scale, action repeat and removing done penalty"""
|
|
|
|
def __init__(self, env, action_repeat=3, reward_scale=5, rm_done=True):
|
|
super().__init__(env)
|
|
self.action_repeat = action_repeat
|
|
self.reward_scale = reward_scale
|
|
self.rm_done = rm_done
|
|
|
|
def step(self, action):
|
|
rew_sum = 0.0
|
|
for _ in range(self.action_repeat):
|
|
obs, rew, done, info = self.env.step(action)
|
|
# remove done reward penalty
|
|
if not done or not self.rm_done:
|
|
rew_sum = rew_sum + rew
|
|
if done:
|
|
break
|
|
# scale reward
|
|
return obs, self.reward_scale * rew_sum, done, info
|
|
|
|
|
|
def test_sac_bipedal(args=get_args()):
|
|
env = Wrapper(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
|
|
args.max_action = env.action_space.high[0]
|
|
|
|
train_envs = SubprocVectorEnv(
|
|
[lambda: Wrapper(gym.make(args.task)) for _ in range(args.training_num)]
|
|
)
|
|
# test_envs = gym.make(args.task)
|
|
test_envs = SubprocVectorEnv(
|
|
[
|
|
lambda: Wrapper(gym.make(args.task), reward_scale=1, rm_done=False)
|
|
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_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
|
|
).to(args.device)
|
|
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
|
|
|
net_c1 = Net(
|
|
args.state_shape,
|
|
args.action_shape,
|
|
hidden_sizes=args.hidden_sizes,
|
|
concat=True,
|
|
device=args.device
|
|
)
|
|
critic1 = Critic(net_c1, device=args.device).to(args.device)
|
|
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
|
|
|
|
net_c2 = Net(
|
|
args.state_shape,
|
|
args.action_shape,
|
|
hidden_sizes=args.hidden_sizes,
|
|
concat=True,
|
|
device=args.device
|
|
)
|
|
critic2 = Critic(net_c2, device=args.device).to(args.device)
|
|
critic2_optim = torch.optim.Adam(critic2.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 = SACPolicy(
|
|
actor,
|
|
actor_optim,
|
|
critic1,
|
|
critic1_optim,
|
|
critic2,
|
|
critic2_optim,
|
|
tau=args.tau,
|
|
gamma=args.gamma,
|
|
alpha=args.alpha,
|
|
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))
|
|
print("Loaded agent from: ", args.resume_path)
|
|
|
|
# collector
|
|
train_collector = Collector(
|
|
policy,
|
|
train_envs,
|
|
VectorReplayBuffer(args.buffer_size, len(train_envs)),
|
|
exploration_noise=True
|
|
)
|
|
test_collector = Collector(policy, test_envs)
|
|
# train_collector.collect(n_step=args.buffer_size)
|
|
# log
|
|
log_path = os.path.join(args.logdir, args.task, 'sac')
|
|
writer = SummaryWriter(log_path)
|
|
logger = TensorboardLogger(writer)
|
|
|
|
def save_best_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,
|
|
update_per_step=args.update_per_step,
|
|
test_in_train=False,
|
|
stop_fn=stop_fn,
|
|
save_best_fn=save_best_fn,
|
|
logger=logger
|
|
)
|
|
|
|
if __name__ == '__main__':
|
|
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)
|
|
rews, lens = result["rews"], result["lens"]
|
|
print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_sac_bipedal()
|