237 lines
8.1 KiB
Python
237 lines
8.1 KiB
Python
|
#!/usr/bin/env python3
|
||
|
|
import argparse
|
||
|
|
import datetime
|
||
|
|
import os
|
||
|
|
import pprint
|
||
|
|
|
||
|
|
import d4rl
|
||
|
|
import gym
|
||
|
|
import numpy as np
|
||
|
|
import torch
|
||
|
|
from torch.utils.tensorboard import SummaryWriter
|
||
|
|
|
||
|
|
from tianshou.data import Batch, Collector, ReplayBuffer, VectorReplayBuffer
|
||
|
|
from tianshou.env import SubprocVectorEnv
|
||
|
|
from tianshou.policy import CQLPolicy
|
||
|
|
from tianshou.trainer import offline_trainer
|
||
|
|
from tianshou.utils import BasicLogger
|
||
|
|
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='halfcheetah-medium-v1')
|
||
|
|
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('--actor-lr', type=float, default=1e-4)
|
||
|
|
parser.add_argument('--critic-lr', type=float, default=3e-4)
|
||
|
|
parser.add_argument('--alpha', type=float, default=0.2)
|
||
|
|
parser.add_argument('--auto-alpha', default=True, action='store_true')
|
||
|
|
parser.add_argument('--alpha-lr', type=float, default=1e-4)
|
||
|
|
parser.add_argument('--cql-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('--n-step', type=int, default=3)
|
||
|
|
parser.add_argument('--batch-size', type=int, default=256)
|
||
|
|
|
||
|
|
parser.add_argument("--tau", type=float, default=0.005)
|
||
|
|
parser.add_argument("--temperature", type=float, default=1.0)
|
||
|
|
parser.add_argument("--cql-weight", type=float, default=1.0)
|
||
|
|
parser.add_argument("--with-lagrange", type=bool, default=True)
|
||
|
|
parser.add_argument("--lagrange-threshold", type=float, default=10.0)
|
||
|
|
parser.add_argument("--gamma", type=float, default=0.99)
|
||
|
|
|
||
|
|
parser.add_argument("--eval-freq", type=int, default=1)
|
||
|
|
parser.add_argument('--training-num', type=int, default=10)
|
||
|
|
parser.add_argument('--test-num', type=int, default=10)
|
||
|
|
parser.add_argument('--logdir', type=str, default='log')
|
||
|
|
parser.add_argument('--render', type=float, default=1 / 35)
|
||
|
|
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(
|
||
|
|
'--watch',
|
||
|
|
default=False,
|
||
|
|
action='store_true',
|
||
|
|
help='watch the play of pre-trained policy only',
|
||
|
|
)
|
||
|
|
return parser.parse_args()
|
||
|
|
|
||
|
|
|
||
|
|
def test_cql():
|
||
|
|
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] # float
|
||
|
|
print("device:", args.device)
|
||
|
|
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))
|
||
|
|
|
||
|
|
args.state_dim = args.state_shape[0]
|
||
|
|
args.action_dim = args.action_shape[0]
|
||
|
|
print("Max_action", args.max_action)
|
||
|
|
|
||
|
|
# train_envs = gym.make(args.task)
|
||
|
|
train_envs = SubprocVectorEnv(
|
||
|
|
[lambda: gym.make(args.task) for _ in range(args.training_num)]
|
||
|
|
)
|
||
|
|
# test_envs = gym.make(args.task)
|
||
|
|
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
|
||
|
|
# actor network
|
||
|
|
net_a = Net(
|
||
|
|
args.state_shape,
|
||
|
|
args.action_shape,
|
||
|
|
hidden_sizes=args.hidden_sizes,
|
||
|
|
device=args.device,
|
||
|
|
)
|
||
|
|
actor = ActorProb(
|
||
|
|
net_a,
|
||
|
|
action_shape=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)
|
||
|
|
|
||
|
|
# critic network
|
||
|
|
net_c1 = Net(
|
||
|
|
args.state_shape,
|
||
|
|
args.action_shape,
|
||
|
|
hidden_sizes=args.hidden_sizes,
|
||
|
|
concat=True,
|
||
|
|
device=args.device,
|
||
|
|
)
|
||
|
|
net_c2 = 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)
|
||
|
|
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 = CQLPolicy(
|
||
|
|
actor,
|
||
|
|
actor_optim,
|
||
|
|
critic1,
|
||
|
|
critic1_optim,
|
||
|
|
critic2,
|
||
|
|
critic2_optim,
|
||
|
|
cql_alpha_lr=args.cql_alpha_lr,
|
||
|
|
cql_weight=args.cql_weight,
|
||
|
|
tau=args.tau,
|
||
|
|
gamma=args.gamma,
|
||
|
|
alpha=args.alpha,
|
||
|
|
temperature=args.temperature,
|
||
|
|
with_lagrange=args.with_lagrange,
|
||
|
|
lagrange_threshold=args.lagrange_threshold,
|
||
|
|
min_action=np.min(env.action_space.low),
|
||
|
|
max_action=np.max(env.action_space.high),
|
||
|
|
device=args.device,
|
||
|
|
)
|
||
|
|
|
||
|
|
# 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
|
||
|
|
t0 = datetime.datetime.now().strftime("%m%d_%H%M%S")
|
||
|
|
log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_cql'
|
||
|
|
log_path = os.path.join(args.logdir, args.task, 'cql', log_file)
|
||
|
|
writer = SummaryWriter(log_path)
|
||
|
|
writer.add_text("args", str(args))
|
||
|
|
logger = BasicLogger(writer)
|
||
|
|
|
||
|
|
def save_fn(policy):
|
||
|
|
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
|
||
|
|
|
||
|
|
def watch():
|
||
|
|
if args.resume_path is None:
|
||
|
|
args.resume_path = os.path.join(log_path, 'policy.pth')
|
||
|
|
|
||
|
|
policy.load_state_dict(
|
||
|
|
torch.load(args.resume_path, map_location=torch.device('cpu'))
|
||
|
|
)
|
||
|
|
policy.eval()
|
||
|
|
collector = Collector(policy, env)
|
||
|
|
collector.collect(n_episode=1, render=1 / 35)
|
||
|
|
|
||
|
|
if not args.watch:
|
||
|
|
dataset = d4rl.qlearning_dataset(env)
|
||
|
|
dataset_size = dataset['rewards'].size
|
||
|
|
|
||
|
|
print("dataset_size", dataset_size)
|
||
|
|
replay_buffer = ReplayBuffer(dataset_size)
|
||
|
|
|
||
|
|
for i in range(dataset_size):
|
||
|
|
replay_buffer.add(
|
||
|
|
Batch(
|
||
|
|
obs=dataset['observations'][i],
|
||
|
|
act=dataset['actions'][i],
|
||
|
|
rew=dataset['rewards'][i],
|
||
|
|
done=dataset['terminals'][i],
|
||
|
|
obs_next=dataset['next_observations'][i],
|
||
|
|
)
|
||
|
|
)
|
||
|
|
print("dataset loaded")
|
||
|
|
# trainer
|
||
|
|
result = offline_trainer(
|
||
|
|
policy,
|
||
|
|
replay_buffer,
|
||
|
|
test_collector,
|
||
|
|
args.epoch,
|
||
|
|
args.step_per_epoch,
|
||
|
|
args.test_num,
|
||
|
|
args.batch_size,
|
||
|
|
save_fn=save_fn,
|
||
|
|
logger=logger,
|
||
|
|
)
|
||
|
|
pprint.pprint(result)
|
||
|
|
else:
|
||
|
|
watch()
|
||
|
|
|
||
|
|
# 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_cql()
|