152 lines
5.2 KiB
Python
152 lines
5.2 KiB
Python
import gym
|
|
import time
|
|
import torch
|
|
import pprint
|
|
import argparse
|
|
import numpy as np
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.policy import PGPolicy
|
|
from tianshou.env import VectorEnv
|
|
from tianshou.trainer import onpolicy_trainer
|
|
from tianshou.data import Batch, Collector, ReplayBuffer
|
|
|
|
if __name__ == '__main__':
|
|
from net import Net
|
|
else: # pytest
|
|
from test.discrete.net import Net
|
|
|
|
|
|
def compute_return_base(batch, aa=None, bb=None, gamma=0.1):
|
|
returns = np.zeros_like(batch.rew)
|
|
last = 0
|
|
for i in reversed(range(len(batch.rew))):
|
|
returns[i] = batch.rew[i]
|
|
if not batch.done[i]:
|
|
returns[i] += last * gamma
|
|
last = returns[i]
|
|
batch.update(returns=returns)
|
|
return batch
|
|
|
|
|
|
def test_fn(size=2560):
|
|
policy = PGPolicy(None, None, None, discount_factor=0.1)
|
|
fn = policy.process_fn
|
|
# fn = compute_return_base
|
|
batch = Batch(
|
|
done=np.array([1, 0, 0, 1, 0, 1, 0, 1.]),
|
|
rew=np.array([0, 1, 2, 3, 4, 5, 6, 7.]),
|
|
)
|
|
batch = fn(batch, None, None)
|
|
ans = np.array([0, 1.23, 2.3, 3, 4.5, 5, 6.7, 7])
|
|
assert abs(batch.returns - ans).sum() <= 1e-5
|
|
batch = Batch(
|
|
done=np.array([0, 1, 0, 1, 0, 1, 0.]),
|
|
rew=np.array([7, 6, 1, 2, 3, 4, 5.]),
|
|
)
|
|
batch = fn(batch, None, None)
|
|
ans = np.array([7.6, 6, 1.2, 2, 3.4, 4, 5])
|
|
assert abs(batch.returns - ans).sum() <= 1e-5
|
|
batch = Batch(
|
|
done=np.array([0, 1, 0, 1, 0, 0, 1.]),
|
|
rew=np.array([7, 6, 1, 2, 3, 4, 5.]),
|
|
)
|
|
batch = fn(batch, None, None)
|
|
ans = np.array([7.6, 6, 1.2, 2, 3.45, 4.5, 5])
|
|
assert abs(batch.returns - ans).sum() <= 1e-5
|
|
if __name__ == '__main__':
|
|
batch = Batch(
|
|
done=np.random.randint(100, size=size) == 0,
|
|
rew=np.random.random(size),
|
|
)
|
|
cnt = 3000
|
|
t = time.time()
|
|
for _ in range(cnt):
|
|
compute_return_base(batch)
|
|
print(f'vanilla: {(time.time() - t) / cnt}')
|
|
t = time.time()
|
|
for _ in range(cnt):
|
|
policy.process_fn(batch, None, None)
|
|
print(f'policy: {(time.time() - t) / cnt}')
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--task', type=str, default='CartPole-v0')
|
|
parser.add_argument('--seed', type=int, default=1626)
|
|
parser.add_argument('--buffer-size', type=int, default=20000)
|
|
parser.add_argument('--lr', type=float, default=3e-4)
|
|
parser.add_argument('--gamma', type=float, default=0.9)
|
|
parser.add_argument('--epoch', type=int, default=100)
|
|
parser.add_argument('--step-per-epoch', type=int, default=1000)
|
|
parser.add_argument('--collect-per-step', type=int, default=10)
|
|
parser.add_argument('--repeat-per-collect', type=int, default=2)
|
|
parser.add_argument('--batch-size', type=int, default=64)
|
|
parser.add_argument('--layer-num', type=int, default=3)
|
|
parser.add_argument('--training-num', type=int, default=8)
|
|
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.)
|
|
parser.add_argument(
|
|
'--device', type=str,
|
|
default='cuda' if torch.cuda.is_available() else 'cpu')
|
|
args = parser.parse_known_args()[0]
|
|
return args
|
|
|
|
|
|
def test_pg(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
|
|
# train_envs = gym.make(args.task)
|
|
train_envs = VectorEnv(
|
|
[lambda: gym.make(args.task) for _ in range(args.training_num)])
|
|
# test_envs = gym.make(args.task)
|
|
test_envs = VectorEnv(
|
|
[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.layer_num, args.state_shape, args.action_shape,
|
|
device=args.device)
|
|
net = net.to(args.device)
|
|
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
|
|
dist = torch.distributions.Categorical
|
|
policy = PGPolicy(net, optim, dist, args.gamma)
|
|
# collector
|
|
train_collector = Collector(
|
|
policy, train_envs, ReplayBuffer(args.buffer_size))
|
|
test_collector = Collector(policy, test_envs)
|
|
# log
|
|
writer = SummaryWriter(args.logdir + '/' + 'ppo')
|
|
|
|
def stop_fn(x):
|
|
return x >= env.spec.reward_threshold
|
|
|
|
# trainer
|
|
result = onpolicy_trainer(
|
|
policy, train_collector, test_collector, args.epoch,
|
|
args.step_per_epoch, args.collect_per_step, args.repeat_per_collect,
|
|
args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer,
|
|
task=args.task)
|
|
assert stop_fn(result['best_reward'])
|
|
train_collector.close()
|
|
test_collector.close()
|
|
if __name__ == '__main__':
|
|
pprint.pprint(result)
|
|
# Let's watch its performance!
|
|
env = gym.make(args.task)
|
|
collector = Collector(policy, env)
|
|
result = collector.collect(n_episode=1, render=args.render)
|
|
print(f'Final reward: {result["rew"]}, length: {result["len"]}')
|
|
collector.close()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
# test_fn()
|
|
test_pg()
|