710966eda7
train_fn(epoch) -> train_fn(epoch, num_env_step) test_fn(epoch) -> test_fn(epoch, num_env_step)
98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
import gym
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import torch
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import pprint
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import argparse
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.policy import PSRLPolicy
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from tianshou.trainer import onpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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from tianshou.env import DummyVectorEnv, SubprocVectorEnv
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--task', type=str, default='NChain-v0')
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--buffer-size', type=int, default=50000)
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parser.add_argument('--epoch', type=int, default=5)
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parser.add_argument('--step-per-epoch', type=int, default=5)
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parser.add_argument('--collect-per-step', type=int, default=1)
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parser.add_argument('--training-num', type=int, default=1)
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parser.add_argument('--test-num', type=int, default=100)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.0)
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parser.add_argument('--rew-mean-prior', type=float, default=0.0)
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parser.add_argument('--rew-std-prior', type=float, default=1.0)
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parser.add_argument('--gamma', type=float, default=0.99)
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parser.add_argument('--eps', type=float, default=0.01)
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parser.add_argument('--add-done-loop', action='store_true')
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return parser.parse_known_args()[0]
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def test_psrl(args=get_args()):
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env = gym.make(args.task)
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if args.task == "NChain-v0":
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env.spec.reward_threshold = 3647 # described in PSRL paper
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print("reward threshold:", env.spec.reward_threshold)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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# train_envs = gym.make(args.task)
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# train_envs = gym.make(args.task)
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train_envs = DummyVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.test_num)])
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# model
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n_action = args.action_shape
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n_state = args.state_shape
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trans_count_prior = np.ones((n_state, n_action, n_state))
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rew_mean_prior = np.full((n_state, n_action), args.rew_mean_prior)
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rew_std_prior = np.full((n_state, n_action), args.rew_std_prior)
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policy = PSRLPolicy(
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trans_count_prior, rew_mean_prior, rew_std_prior, args.gamma, args.eps,
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args.add_done_loop)
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# collector
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train_collector = Collector(
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policy, train_envs, ReplayBuffer(args.buffer_size))
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test_collector = Collector(policy, test_envs)
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# log
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writer = SummaryWriter(args.logdir + '/' + args.task)
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def stop_fn(mean_rewards):
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if env.spec.reward_threshold:
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return mean_rewards >= env.spec.reward_threshold
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else:
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return False
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train_collector.collect(n_step=args.buffer_size, random=True)
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# trainer
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result = onpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, 1,
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args.test_num, 0, stop_fn=stop_fn, writer=writer,
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test_in_train=False)
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if __name__ == '__main__':
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pprint.pprint(result)
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# Let's watch its performance!
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policy.eval()
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test_envs.seed(args.seed)
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test_collector.reset()
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result = test_collector.collect(n_episode=[1] * args.test_num,
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render=args.render)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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elif env.spec.reward_threshold:
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assert result["best_reward"] >= env.spec.reward_threshold
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if __name__ == '__main__':
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test_psrl()
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