12d4262f80
A test is not a script and should not be used as such Also marked pistonball test as skipped since it doesn't actually test anything
179 lines
6.7 KiB
Python
179 lines
6.7 KiB
Python
import argparse
|
|
import os
|
|
|
|
import gymnasium as gym
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.data import (
|
|
Collector,
|
|
PrioritizedVectorReplayBuffer,
|
|
ReplayBuffer,
|
|
VectorReplayBuffer,
|
|
)
|
|
from tianshou.env import DummyVectorEnv
|
|
from tianshou.policy import FQFPolicy
|
|
from tianshou.policy.base import BasePolicy
|
|
from tianshou.trainer import OffpolicyTrainer
|
|
from tianshou.utils import TensorboardLogger
|
|
from tianshou.utils.net.common import Net
|
|
from tianshou.utils.net.discrete import FractionProposalNetwork, FullQuantileFunction
|
|
from tianshou.utils.space_info import SpaceInfo
|
|
|
|
|
|
def get_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--task", type=str, default="CartPole-v1")
|
|
parser.add_argument("--reward-threshold", type=float, default=None)
|
|
parser.add_argument("--seed", type=int, default=1)
|
|
parser.add_argument("--eps-test", type=float, default=0.05)
|
|
parser.add_argument("--eps-train", type=float, default=0.1)
|
|
parser.add_argument("--buffer-size", type=int, default=20000)
|
|
parser.add_argument("--lr", type=float, default=3e-3)
|
|
parser.add_argument("--fraction-lr", type=float, default=2.5e-9)
|
|
parser.add_argument("--gamma", type=float, default=0.9)
|
|
parser.add_argument("--num-fractions", type=int, default=32)
|
|
parser.add_argument("--num-cosines", type=int, default=64)
|
|
parser.add_argument("--ent-coef", type=float, default=10.0)
|
|
parser.add_argument("--n-step", type=int, default=3)
|
|
parser.add_argument("--target-update-freq", type=int, default=320)
|
|
parser.add_argument("--epoch", type=int, default=10)
|
|
parser.add_argument("--step-per-epoch", type=int, default=10000)
|
|
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=64)
|
|
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64, 64])
|
|
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=0.0)
|
|
parser.add_argument("--prioritized-replay", action="store_true", default=False)
|
|
parser.add_argument("--alpha", type=float, default=0.6)
|
|
parser.add_argument("--beta", type=float, default=0.4)
|
|
parser.add_argument(
|
|
"--device",
|
|
type=str,
|
|
default="cuda" if torch.cuda.is_available() else "cpu",
|
|
)
|
|
return parser.parse_known_args()[0]
|
|
|
|
|
|
def test_fqf(args: argparse.Namespace = get_args()) -> None:
|
|
env = gym.make(args.task)
|
|
space_info = SpaceInfo.from_env(env)
|
|
assert isinstance(env.action_space, gym.spaces.Discrete)
|
|
args.state_shape = space_info.observation_info.obs_shape
|
|
args.action_shape = space_info.action_info.action_shape
|
|
if args.reward_threshold is None:
|
|
default_reward_threshold = {"CartPole-v1": 195}
|
|
args.reward_threshold = default_reward_threshold.get(
|
|
args.task,
|
|
env.spec.reward_threshold if env.spec else None,
|
|
)
|
|
# train_envs = gym.make(args.task)
|
|
# you can also use tianshou.env.SubprocVectorEnv
|
|
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
|
|
feature_net = Net(
|
|
args.state_shape,
|
|
args.hidden_sizes[-1],
|
|
hidden_sizes=args.hidden_sizes[:-1],
|
|
device=args.device,
|
|
softmax=False,
|
|
)
|
|
net = FullQuantileFunction(
|
|
feature_net,
|
|
args.action_shape,
|
|
args.hidden_sizes,
|
|
num_cosines=args.num_cosines,
|
|
device=args.device,
|
|
)
|
|
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
|
|
fraction_net = FractionProposalNetwork(args.num_fractions, net.input_dim)
|
|
fraction_optim = torch.optim.RMSprop(fraction_net.parameters(), lr=args.fraction_lr)
|
|
policy: FQFPolicy = FQFPolicy(
|
|
model=net,
|
|
optim=optim,
|
|
fraction_model=fraction_net,
|
|
fraction_optim=fraction_optim,
|
|
action_space=env.action_space,
|
|
discount_factor=args.gamma,
|
|
num_fractions=args.num_fractions,
|
|
ent_coef=args.ent_coef,
|
|
estimation_step=args.n_step,
|
|
target_update_freq=args.target_update_freq,
|
|
).to(args.device)
|
|
# buffer
|
|
buf: ReplayBuffer
|
|
if args.prioritized_replay:
|
|
buf = PrioritizedVectorReplayBuffer(
|
|
args.buffer_size,
|
|
buffer_num=len(train_envs),
|
|
alpha=args.alpha,
|
|
beta=args.beta,
|
|
)
|
|
else:
|
|
buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs))
|
|
# collector
|
|
train_collector = Collector(policy, train_envs, buf, exploration_noise=True)
|
|
test_collector = Collector(policy, test_envs, exploration_noise=True)
|
|
# policy.set_eps(1)
|
|
train_collector.reset()
|
|
train_collector.collect(n_step=args.batch_size * args.training_num)
|
|
# log
|
|
log_path = os.path.join(args.logdir, args.task, "fqf")
|
|
writer = SummaryWriter(log_path)
|
|
logger = TensorboardLogger(writer)
|
|
|
|
def save_best_fn(policy: BasePolicy) -> None:
|
|
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
|
|
|
|
def stop_fn(mean_rewards: float) -> bool:
|
|
return mean_rewards >= args.reward_threshold
|
|
|
|
def train_fn(epoch: int, env_step: int) -> None:
|
|
# eps annnealing, just a demo
|
|
if env_step <= 10000:
|
|
policy.set_eps(args.eps_train)
|
|
elif env_step <= 50000:
|
|
eps = args.eps_train - (env_step - 10000) / 40000 * (0.9 * args.eps_train)
|
|
policy.set_eps(eps)
|
|
else:
|
|
policy.set_eps(0.1 * args.eps_train)
|
|
|
|
def test_fn(epoch: int, env_step: int | None) -> None:
|
|
policy.set_eps(args.eps_test)
|
|
|
|
# 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,
|
|
train_fn=train_fn,
|
|
test_fn=test_fn,
|
|
stop_fn=stop_fn,
|
|
save_best_fn=save_best_fn,
|
|
logger=logger,
|
|
update_per_step=args.update_per_step,
|
|
).run()
|
|
assert stop_fn(result.best_reward)
|
|
|
|
|
|
def test_pfqf(args: argparse.Namespace = get_args()) -> None:
|
|
args.prioritized_replay = True
|
|
args.gamma = 0.95
|
|
test_fqf(args)
|