b900fdf6f2
Closes #947 This removes all kwargs from all policy constructors. While doing that, I also improved several names and added a whole lot of TODOs. ## Functional changes: 1. Added possibility to pass None as `critic2` and `critic2_optim`. In fact, the default behavior then should cover the absolute majority of cases 2. Added a function called `clone_optimizer` as a temporary measure to support passing `critic2_optim=None` ## Breaking changes: 1. `action_space` is no longer optional. In fact, it already was non-optional, as there was a ValueError in BasePolicy.init. So now several examples were fixed to reflect that 2. `reward_normalization` removed from DDPG and children. It was never allowed to pass it as `True` there, an error would have been raised in `compute_n_step_reward`. Now I removed it from the interface 3. renamed `critic1` and similar to `critic`, in order to have uniform interfaces. Note that the `critic` in DDPG was optional for the sole reason that child classes used `critic1`. I removed this optionality (DDPG can't do anything with `critic=None`) 4. Several renamings of fields (mostly private to public, so backwards compatible) ## Additional changes: 1. Removed type and default declaration from docstring. This kind of duplication is really not necessary 2. Policy constructors are now only called using named arguments, not a fragile mixture of positional and named as before 5. Minor beautifications in typing and code 6. Generally shortened docstrings and made them uniform across all policies (hopefully) ## Comment: With these changes, several problems in tianshou's inheritance hierarchy become more apparent. I tried highlighting them for future work. --------- Co-authored-by: Dominik Jain <d.jain@appliedai.de>
232 lines
9.0 KiB
Python
232 lines
9.0 KiB
Python
import argparse
|
|
import os
|
|
import pickle
|
|
import pprint
|
|
|
|
import gymnasium as gym
|
|
import numpy as np
|
|
import torch
|
|
from torch.distributions import Independent, Normal
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.data import Collector, VectorReplayBuffer
|
|
from tianshou.env import DummyVectorEnv
|
|
from tianshou.policy import GAILPolicy
|
|
from tianshou.trainer import OnpolicyTrainer
|
|
from tianshou.utils import TensorboardLogger
|
|
from tianshou.utils.net.common import ActorCritic, Net
|
|
from tianshou.utils.net.continuous import ActorProb, Critic
|
|
|
|
if __name__ == "__main__":
|
|
from gather_pendulum_data import expert_file_name, gather_data
|
|
else: # pytest
|
|
from test.offline.gather_pendulum_data import expert_file_name, gather_data
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--task", type=str, default="Pendulum-v1")
|
|
parser.add_argument("--reward-threshold", type=float, default=None)
|
|
parser.add_argument("--seed", type=int, default=1)
|
|
parser.add_argument("--buffer-size", type=int, default=20000)
|
|
parser.add_argument("--lr", type=float, default=1e-3)
|
|
parser.add_argument("--disc-lr", type=float, default=5e-4)
|
|
parser.add_argument("--gamma", type=float, default=0.95)
|
|
parser.add_argument("--epoch", type=int, default=5)
|
|
parser.add_argument("--step-per-epoch", type=int, default=150000)
|
|
parser.add_argument("--episode-per-collect", type=int, default=16)
|
|
parser.add_argument("--repeat-per-collect", type=int, default=2)
|
|
parser.add_argument("--disc-update-num", type=int, default=2)
|
|
parser.add_argument("--batch-size", type=int, default=128)
|
|
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
|
|
parser.add_argument("--training-num", type=int, default=16)
|
|
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(
|
|
"--device",
|
|
type=str,
|
|
default="cuda" if torch.cuda.is_available() else "cpu",
|
|
)
|
|
# ppo special
|
|
parser.add_argument("--vf-coef", type=float, default=0.25)
|
|
parser.add_argument("--ent-coef", type=float, default=0.0)
|
|
parser.add_argument("--eps-clip", type=float, default=0.2)
|
|
parser.add_argument("--max-grad-norm", type=float, default=0.5)
|
|
parser.add_argument("--gae-lambda", type=float, default=0.95)
|
|
parser.add_argument("--rew-norm", type=int, default=1)
|
|
parser.add_argument("--dual-clip", type=float, default=None)
|
|
parser.add_argument("--value-clip", type=int, default=1)
|
|
parser.add_argument("--norm-adv", type=int, default=1)
|
|
parser.add_argument("--recompute-adv", type=int, default=0)
|
|
parser.add_argument("--resume", action="store_true")
|
|
parser.add_argument("--save-interval", type=int, default=4)
|
|
parser.add_argument("--load-buffer-name", type=str, default=expert_file_name())
|
|
return parser.parse_known_args()[0]
|
|
|
|
|
|
def test_gail(args=get_args()):
|
|
if os.path.exists(args.load_buffer_name) and os.path.isfile(args.load_buffer_name):
|
|
if args.load_buffer_name.endswith(".hdf5"):
|
|
buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name)
|
|
else:
|
|
with open(args.load_buffer_name, "rb") as f:
|
|
buffer = pickle.load(f)
|
|
else:
|
|
buffer = gather_data()
|
|
env = gym.make(args.task)
|
|
if args.reward_threshold is None:
|
|
default_reward_threshold = {"Pendulum-v0": -1100, "Pendulum-v1": -1100}
|
|
args.reward_threshold = default_reward_threshold.get(args.task, env.spec.reward_threshold)
|
|
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]
|
|
# you can also use tianshou.env.SubprocVectorEnv
|
|
# train_envs = gym.make(args.task)
|
|
train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)])
|
|
# test_envs = gym.make(args.task)
|
|
test_envs = DummyVectorEnv([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 = ActorProb(net, args.action_shape, max_action=args.max_action, device=args.device).to(
|
|
args.device,
|
|
)
|
|
critic = Critic(
|
|
Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device),
|
|
device=args.device,
|
|
).to(args.device)
|
|
actor_critic = ActorCritic(actor, critic)
|
|
# orthogonal initialization
|
|
for m in actor_critic.modules():
|
|
if isinstance(m, torch.nn.Linear):
|
|
torch.nn.init.orthogonal_(m.weight)
|
|
torch.nn.init.zeros_(m.bias)
|
|
optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
|
|
# discriminator
|
|
disc_net = Critic(
|
|
Net(
|
|
args.state_shape,
|
|
action_shape=args.action_shape,
|
|
hidden_sizes=args.hidden_sizes,
|
|
activation=torch.nn.Tanh,
|
|
device=args.device,
|
|
concat=True,
|
|
),
|
|
device=args.device,
|
|
).to(args.device)
|
|
for m in disc_net.modules():
|
|
if isinstance(m, torch.nn.Linear):
|
|
# orthogonal initialization
|
|
torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2))
|
|
torch.nn.init.zeros_(m.bias)
|
|
disc_optim = torch.optim.Adam(disc_net.parameters(), lr=args.disc_lr)
|
|
|
|
# replace DiagGuassian with Independent(Normal) which is equivalent
|
|
# pass *logits to be consistent with policy.forward
|
|
def dist(*logits):
|
|
return Independent(Normal(*logits), 1)
|
|
|
|
policy = GAILPolicy(
|
|
actor=actor,
|
|
critic=critic,
|
|
optim=optim,
|
|
dist_fn=dist,
|
|
expert_buffer=buffer,
|
|
disc_net=disc_net,
|
|
disc_optim=disc_optim,
|
|
disc_update_num=args.disc_update_num,
|
|
discount_factor=args.gamma,
|
|
max_grad_norm=args.max_grad_norm,
|
|
eps_clip=args.eps_clip,
|
|
vf_coef=args.vf_coef,
|
|
ent_coef=args.ent_coef,
|
|
reward_normalization=args.rew_norm,
|
|
advantage_normalization=args.norm_adv,
|
|
recompute_advantage=args.recompute_adv,
|
|
dual_clip=args.dual_clip,
|
|
value_clip=args.value_clip,
|
|
gae_lambda=args.gae_lambda,
|
|
action_space=env.action_space,
|
|
)
|
|
# collector
|
|
train_collector = Collector(
|
|
policy,
|
|
train_envs,
|
|
VectorReplayBuffer(args.buffer_size, len(train_envs)),
|
|
)
|
|
test_collector = Collector(policy, test_envs)
|
|
# log
|
|
log_path = os.path.join(args.logdir, args.task, "gail")
|
|
writer = SummaryWriter(log_path)
|
|
logger = TensorboardLogger(writer, save_interval=args.save_interval)
|
|
|
|
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 >= args.reward_threshold
|
|
|
|
def save_checkpoint_fn(epoch, env_step, gradient_step):
|
|
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
|
|
ckpt_path = os.path.join(log_path, "checkpoint.pth")
|
|
# Example: saving by epoch num
|
|
# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
|
|
torch.save(
|
|
{
|
|
"model": policy.state_dict(),
|
|
"optim": optim.state_dict(),
|
|
},
|
|
ckpt_path,
|
|
)
|
|
return ckpt_path
|
|
|
|
if args.resume:
|
|
# load from existing checkpoint
|
|
print(f"Loading agent under {log_path}")
|
|
ckpt_path = os.path.join(log_path, "checkpoint.pth")
|
|
if os.path.exists(ckpt_path):
|
|
checkpoint = torch.load(ckpt_path, map_location=args.device)
|
|
policy.load_state_dict(checkpoint["model"])
|
|
optim.load_state_dict(checkpoint["optim"])
|
|
print("Successfully restore policy and optim.")
|
|
else:
|
|
print("Fail to restore policy and optim.")
|
|
|
|
# trainer
|
|
result = OnpolicyTrainer(
|
|
policy=policy,
|
|
train_collector=train_collector,
|
|
test_collector=test_collector,
|
|
max_epoch=args.epoch,
|
|
step_per_epoch=args.step_per_epoch,
|
|
repeat_per_collect=args.repeat_per_collect,
|
|
episode_per_test=args.test_num,
|
|
batch_size=args.batch_size,
|
|
episode_per_collect=args.episode_per_collect,
|
|
stop_fn=stop_fn,
|
|
save_best_fn=save_best_fn,
|
|
logger=logger,
|
|
resume_from_log=args.resume,
|
|
save_checkpoint_fn=save_checkpoint_fn,
|
|
).run()
|
|
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_gail()
|