#!/usr/bin/env python3
# isort: skip_file
import argparse
import datetime
import os
import pprint
import gymnasium as gym
import numpy as np
import torch
import wandb
from torch.utils.tensorboard import SummaryWriter
from tianshou.data import (
Collector,
HERReplayBuffer,
HERVectorReplayBuffer,
ReplayBuffer,
VectorReplayBuffer,
)
from tianshou.env import ShmemVectorEnv, TruncatedAsTerminated
from tianshou.exploration import GaussianNoise
from tianshou.policy import DDPGPolicy
from tianshou.trainer import OffpolicyTrainer
from tianshou.utils import TensorboardLogger, WandbLogger
from tianshou.utils.net.common import Net, get_dict_state_decorator
from tianshou.utils.net.continuous import Actor, Critic
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--task", type=str, default="FetchReach-v3")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--buffer-size", type=int, default=100000)
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[256, 256])
parser.add_argument("--actor-lr", type=float, default=1e-3)
parser.add_argument("--critic-lr", type=float, default=3e-3)
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("--start-timesteps", type=int, default=25000)
parser.add_argument("--epoch", type=int, default=10)
parser.add_argument("--step-per-epoch", type=int, default=5000)
parser.add_argument("--step-per-collect", type=int, default=1)
parser.add_argument("--update-per-step", type=int, default=1)
parser.add_argument("--n-step", type=int, default=1)
parser.add_argument("--batch-size", type=int, default=512)
parser.add_argument("--replay-buffer", type=str, default="her", choices=["normal", "her"])
parser.add_argument("--her-horizon", type=int, default=50)
parser.add_argument("--her-future-k", type=int, default=8)
parser.add_argument("--training-num", type=int, default=1)
parser.add_argument("--test-num", type=int, default=10)
parser.add_argument("--logdir", type=str, default="log")
parser.add_argument("--render", type=float, default=0.0)
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("--resume-id", type=str, default=None)
parser.add_argument(
"--logger",
type=str,
default="tensorboard",
choices=["tensorboard", "wandb"],
)
parser.add_argument("--wandb-project", type=str, default="HER-benchmark")
parser.add_argument(
"--watch",
default=False,
action="store_true",
help="watch the play of pre-trained policy only",
)
return parser.parse_args()
def make_fetch_env(task, training_num, test_num):
env = TruncatedAsTerminated(gym.make(task))
train_envs = ShmemVectorEnv(
[lambda: TruncatedAsTerminated(gym.make(task)) for _ in range(training_num)],
)
test_envs = ShmemVectorEnv(
[lambda: TruncatedAsTerminated(gym.make(task)) for _ in range(test_num)],
)
return env, train_envs, test_envs
def test_ddpg(args=get_args()):
# log
now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
args.algo_name = "ddpg"
log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
log_path = os.path.join(args.logdir, log_name)
# logger
if args.logger == "wandb":
logger = WandbLogger(
save_interval=1,
name=log_name.replace(os.path.sep, "__"),
run_id=args.resume_id,
config=args,
project=args.wandb_project,
)
logger.wandb_run.config.setdefaults(vars(args))
args = argparse.Namespace(**wandb.config)
writer = SummaryWriter(log_path)
writer.add_text("args", str(args))
if args.logger == "tensorboard":
logger = TensorboardLogger(writer)
else: # wandb
logger.load(writer)
env, train_envs, test_envs = make_fetch_env(args.task, args.training_num, args.test_num)
args.state_shape = {
"observation": env.observation_space["observation"].shape,
"achieved_goal": env.observation_space["achieved_goal"].shape,
"desired_goal": env.observation_space["desired_goal"].shape,
}
args.action_shape = env.action_space.shape or env.action_space.n
args.max_action = env.action_space.high[0]
args.exploration_noise = args.exploration_noise * args.max_action
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))
# seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# model
dict_state_dec, flat_state_shape = get_dict_state_decorator(
state_shape=args.state_shape,
keys=["observation", "achieved_goal", "desired_goal"],
)
net_a = dict_state_dec(Net)(
flat_state_shape,
hidden_sizes=args.hidden_sizes,
device=args.device,
)
actor = dict_state_dec(Actor)(
net_a,
args.action_shape,
max_action=args.max_action,
device=args.device,
).to(args.device)
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
net_c = dict_state_dec(Net)(
flat_state_shape,
action_shape=args.action_shape,
hidden_sizes=args.hidden_sizes,
concat=True,
device=args.device,
)
critic = dict_state_dec(Critic)(net_c, device=args.device).to(args.device)
critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
policy = DDPGPolicy(
actor=actor,
actor_optim=actor_optim,
critic=critic,
critic_optim=critic_optim,
tau=args.tau,
gamma=args.gamma,
exploration_noise=GaussianNoise(sigma=args.exploration_noise),
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, map_location=args.device))
print("Loaded agent from: ", args.resume_path)
# collector
def compute_reward_fn(ag: np.ndarray, g: np.ndarray):
return env.compute_reward(ag, g, {})
if args.replay_buffer == "normal":
if args.training_num > 1:
buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
else:
buffer = ReplayBuffer(args.buffer_size)
else:
if args.training_num > 1:
buffer = HERVectorReplayBuffer(
args.buffer_size,
len(train_envs),
compute_reward_fn=compute_reward_fn,
horizon=args.her_horizon,
future_k=args.her_future_k,
)
else:
buffer = HERReplayBuffer(
args.buffer_size,
compute_reward_fn=compute_reward_fn,
horizon=args.her_horizon,
future_k=args.her_future_k,
)
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)
def save_best_fn(policy):
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
if not args.watch:
# trainer
result = OffpolicyTrainer(
policy=policy,
train_collector=train_collector,
test_collector=test_collector,
max_epoch=args.epoch,
step_per_epoch=args.step_per_epoch,
step_per_collect=args.step_per_collect,
episode_per_test=args.test_num,
batch_size=args.batch_size,
save_best_fn=save_best_fn,
logger=logger,
update_per_step=args.update_per_step,
test_in_train=False,
).run()
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)
print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')
if __name__ == "__main__":
test_ddpg()