159 lines
6.1 KiB
Python
159 lines
6.1 KiB
Python
import gym
|
|
import torch
|
|
import argparse
|
|
import numpy as np
|
|
from torch import nn
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.policy import DDPGPolicy
|
|
from tianshou.trainer import step_trainer
|
|
from tianshou.data import Collector, ReplayBuffer
|
|
from tianshou.env import VectorEnv, SubprocVectorEnv
|
|
|
|
|
|
class Actor(nn.Module):
|
|
def __init__(self, layer_num, state_shape, action_shape,
|
|
max_action, device='cpu'):
|
|
super().__init__()
|
|
self.device = device
|
|
self.model = [
|
|
nn.Linear(np.prod(state_shape), 128),
|
|
nn.ReLU(inplace=True)]
|
|
for i in range(layer_num):
|
|
self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
|
|
self.model += [nn.Linear(128, np.prod(action_shape))]
|
|
self.model = nn.Sequential(*self.model)
|
|
self._max = max_action
|
|
|
|
def forward(self, s, **kwargs):
|
|
s = torch.tensor(s, device=self.device, dtype=torch.float)
|
|
batch = s.shape[0]
|
|
s = s.view(batch, -1)
|
|
logits = self.model(s)
|
|
logits = self._max * torch.tanh(logits)
|
|
return logits, None
|
|
|
|
|
|
class Critic(nn.Module):
|
|
def __init__(self, layer_num, state_shape, action_shape, device='cpu'):
|
|
super().__init__()
|
|
self.device = device
|
|
self.model = [
|
|
nn.Linear(np.prod(state_shape) + np.prod(action_shape), 128),
|
|
nn.ReLU(inplace=True)]
|
|
for i in range(layer_num):
|
|
self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
|
|
self.model += [nn.Linear(128, 1)]
|
|
self.model = nn.Sequential(*self.model)
|
|
|
|
def forward(self, s, a):
|
|
s = torch.tensor(s, device=self.device, dtype=torch.float)
|
|
if isinstance(a, np.ndarray):
|
|
a = torch.tensor(a, device=self.device, dtype=torch.float)
|
|
batch = s.shape[0]
|
|
s = s.view(batch, -1)
|
|
a = a.view(batch, -1)
|
|
logits = self.model(torch.cat([s, a], dim=1))
|
|
return logits
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--task', type=str, default='Pendulum-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('--actor-wd', type=float, default=0)
|
|
parser.add_argument('--critic-lr', type=float, default=1e-3)
|
|
parser.add_argument('--critic-wd', type=float, default=1e-2)
|
|
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('--epoch', type=int, default=100)
|
|
parser.add_argument('--step-per-epoch', type=int, default=2400)
|
|
parser.add_argument('--collect-per-step', type=int, default=1)
|
|
parser.add_argument('--batch-size', type=int, default=128)
|
|
parser.add_argument('--layer-num', type=int, default=1)
|
|
parser.add_argument('--training-num', type=int, default=1)
|
|
parser.add_argument('--test-num', type=int, default=100)
|
|
parser.add_argument('--logdir', type=str, default='log')
|
|
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_ddpg(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
|
|
args.max_action = env.action_space.high[0]
|
|
# train_envs = gym.make(args.task)
|
|
train_envs = VectorEnv(
|
|
[lambda: gym.make(args.task) for _ in range(args.training_num)],
|
|
reset_after_done=True)
|
|
# test_envs = gym.make(args.task)
|
|
test_envs = SubprocVectorEnv(
|
|
[lambda: gym.make(args.task) for _ in range(args.test_num)],
|
|
reset_after_done=False)
|
|
# seed
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
train_envs.seed(args.seed)
|
|
test_envs.seed(args.seed)
|
|
# model
|
|
actor = Actor(
|
|
args.layer_num, args.state_shape, args.action_shape,
|
|
args.max_action, args.device
|
|
).to(args.device)
|
|
actor_optim = torch.optim.Adam(
|
|
actor.parameters(), lr=args.actor_lr, weight_decay=args.actor_wd)
|
|
critic = Critic(
|
|
args.layer_num, args.state_shape, args.action_shape, args.device
|
|
).to(args.device)
|
|
critic_optim = torch.optim.Adam(
|
|
critic.parameters(), lr=args.critic_lr, weight_decay=args.critic_wd)
|
|
policy = DDPGPolicy(
|
|
actor, actor_optim, critic, critic_optim,
|
|
[env.action_space.low[0], env.action_space.high[0]],
|
|
args.tau, args.gamma, args.exploration_noise)
|
|
# collector
|
|
train_collector = Collector(
|
|
policy, train_envs, ReplayBuffer(args.buffer_size), 1)
|
|
test_collector = Collector(policy, test_envs, stat_size=args.test_num)
|
|
# log
|
|
writer = SummaryWriter(args.logdir)
|
|
|
|
def stop_fn(x):
|
|
if args.task == 'Pendulum-v0':
|
|
return x >= -250
|
|
else:
|
|
return False
|
|
|
|
# trainer
|
|
train_step, train_episode, test_step, test_episode, best_rew, duration = \
|
|
step_trainer(
|
|
policy, train_collector, test_collector, args.epoch,
|
|
args.step_per_epoch, args.collect_per_step, args.test_num,
|
|
args.batch_size, stop_fn=stop_fn, writer=writer)
|
|
if args.task == 'Pendulum-v0':
|
|
assert stop_fn(best_rew)
|
|
train_collector.close()
|
|
test_collector.close()
|
|
if __name__ == '__main__':
|
|
print(f'Collect {train_step} frame / {train_episode} episode during '
|
|
f'training and {test_step} frame / {test_episode} episode during'
|
|
f' test in {duration:.2f}s, best_reward: {best_rew}, speed: '
|
|
f'{(train_step + test_step) / duration:.2f}it/s')
|
|
# Let's watch its performance!
|
|
env = gym.make(args.task)
|
|
collector = Collector(policy, env)
|
|
result = collector.collect(n_episode=1, render=1 / 35)
|
|
print(f'Final reward: {result["rew"]}, length: {result["len"]}')
|
|
collector.close()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_ddpg()
|