df3d7f582b
* Use `global_step` as the x-axis for wandb * Use Tensorboard SummaryWritter as core with `wandb.init(..., sync_tensorboard=True)` * Update all atari examples with wandb Co-authored-by: Jiayi Weng <trinkle23897@gmail.com>
128 lines
4.5 KiB
Python
128 lines
4.5 KiB
Python
import argparse
|
|
import os
|
|
import pprint
|
|
|
|
import envpool
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.data import Collector, VectorReplayBuffer
|
|
from tianshou.policy import PSRLPolicy
|
|
from tianshou.trainer import onpolicy_trainer
|
|
from tianshou.utils import LazyLogger, TensorboardLogger, WandbLogger
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--task', type=str, default='NChain-v0')
|
|
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=50000)
|
|
parser.add_argument('--epoch', type=int, default=5)
|
|
parser.add_argument('--step-per-epoch', type=int, default=1000)
|
|
parser.add_argument('--episode-per-collect', type=int, default=1)
|
|
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('--rew-mean-prior', type=float, default=0.0)
|
|
parser.add_argument('--rew-std-prior', type=float, default=1.0)
|
|
parser.add_argument('--gamma', type=float, default=0.99)
|
|
parser.add_argument('--eps', type=float, default=0.01)
|
|
parser.add_argument('--add-done-loop', action="store_true", default=False)
|
|
parser.add_argument(
|
|
'--logger',
|
|
type=str,
|
|
default="wandb",
|
|
choices=["wandb", "tensorboard", "none"],
|
|
)
|
|
return parser.parse_known_args()[0]
|
|
|
|
|
|
def test_psrl(args=get_args()):
|
|
train_envs = env = envpool.make_gym(
|
|
args.task, num_envs=args.training_num, seed=args.seed
|
|
)
|
|
test_envs = envpool.make_gym(args.task, num_envs=args.test_num, seed=args.seed)
|
|
if args.reward_threshold is None:
|
|
default_reward_threshold = {"NChain-v0": 3400}
|
|
args.reward_threshold = default_reward_threshold.get(
|
|
args.task, env.spec.reward_threshold
|
|
)
|
|
print("reward threshold:", args.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
|
|
# seed
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
# model
|
|
n_action = args.action_shape
|
|
n_state = args.state_shape
|
|
trans_count_prior = np.ones((n_state, n_action, n_state))
|
|
rew_mean_prior = np.full((n_state, n_action), args.rew_mean_prior)
|
|
rew_std_prior = np.full((n_state, n_action), args.rew_std_prior)
|
|
policy = PSRLPolicy(
|
|
trans_count_prior, rew_mean_prior, rew_std_prior, args.gamma, args.eps,
|
|
args.add_done_loop
|
|
)
|
|
# collector
|
|
train_collector = Collector(
|
|
policy,
|
|
train_envs,
|
|
VectorReplayBuffer(args.buffer_size, len(train_envs)),
|
|
exploration_noise=True
|
|
)
|
|
test_collector = Collector(policy, test_envs)
|
|
# Logger
|
|
if args.logger == "wandb":
|
|
logger = WandbLogger(
|
|
save_interval=1, project='psrl', name='wandb_test', config=args
|
|
)
|
|
if args.logger != "none":
|
|
log_path = os.path.join(args.logdir, args.task, 'psrl')
|
|
writer = SummaryWriter(log_path)
|
|
writer.add_text("args", str(args))
|
|
if args.logger == "tensorboard":
|
|
logger = TensorboardLogger(writer)
|
|
else:
|
|
logger.load(writer)
|
|
else:
|
|
logger = LazyLogger()
|
|
|
|
def stop_fn(mean_rewards):
|
|
return mean_rewards >= args.reward_threshold
|
|
|
|
train_collector.collect(n_step=args.buffer_size, random=True)
|
|
# trainer, test it without logger
|
|
result = onpolicy_trainer(
|
|
policy,
|
|
train_collector,
|
|
test_collector,
|
|
args.epoch,
|
|
args.step_per_epoch,
|
|
1,
|
|
args.test_num,
|
|
0,
|
|
episode_per_collect=args.episode_per_collect,
|
|
stop_fn=stop_fn,
|
|
logger=logger,
|
|
test_in_train=False,
|
|
)
|
|
|
|
if __name__ == '__main__':
|
|
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)
|
|
rews, lens = result["rews"], result["lens"]
|
|
print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
|
|
elif env.spec.reward_threshold:
|
|
assert result["best_reward"] >= env.spec.reward_threshold
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_psrl()
|