add an example of bullet env (experiment from jiqizhixin) (#15)
* add_pybullet_ens_test test on pybullet envs modify some log config * delete DS_Store file * add pybullet_envs test add HalfCheetahBulletEnv-v0 test modify log config * fix pep 8 errors * add pybullet to dev * delete a line * by pass F401 * add log_interval to onpolicy_trainer * add comments * Update halfcheetahBullet_v0_sac.py
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examples/halfcheetahBullet_v0_sac.py
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120
examples/halfcheetahBullet_v0_sac.py
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import os
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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.env import SubprocVectorEnv
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from tianshou.policy import SACPolicy
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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try:
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import pybullet_envs
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except ImportError:
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pass
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from continuous_net import ActorProb, Critic
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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='HalfCheetahBulletEnv-v0')
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parser.add_argument('--run-id', type=str, default='test')
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--buffer-size', type=int, default=20000)
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parser.add_argument('--actor-lr', type=float, default=3e-4)
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parser.add_argument('--critic-lr', type=float, default=1e-3)
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parser.add_argument('--gamma', type=float, default=0.99)
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parser.add_argument('--tau', type=float, default=0.005)
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parser.add_argument('--alpha', type=float, default=0.2)
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parser.add_argument('--epoch', type=int, default=200)
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parser.add_argument('--step-per-epoch', type=int, default=1000)
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parser.add_argument('--collect-per-step', type=int, default=10)
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parser.add_argument('--batch-size', type=int, default=128)
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parser.add_argument('--layer-num', type=int, default=1)
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parser.add_argument('--training-num', type=int, default=8)
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parser.add_argument('--test-num', type=int, default=4)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--log-interval', type=int, default=100)
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parser.add_argument('--render', type=float, default=0.)
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parser.add_argument(
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'--device', type=str,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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args = parser.parse_known_args()[0]
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return args
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def test_sac(args=get_args()):
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torch.set_num_threads(1)
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env = gym.make(args.task)
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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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args.max_action = env.action_space.high[0]
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# you can also use tianshou.env.SubprocVectorEnv
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# train_envs = gym.make(args.task)
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train_envs = SubprocVectorEnv(
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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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actor = ActorProb(
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args.layer_num, args.state_shape, args.action_shape,
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args.max_action, args.device
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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critic1 = Critic(
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args.layer_num, args.state_shape, args.action_shape, args.device
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).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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critic2 = Critic(
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args.layer_num, args.state_shape, args.action_shape, args.device
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).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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policy = SACPolicy(
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actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
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args.tau, args.gamma, args.alpha,
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[env.action_space.low[0], env.action_space.high[0]],
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reward_normalization=True, ignore_done=True)
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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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# train_collector.collect(n_step=args.buffer_size)
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# log
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log_path = os.path.join(args.logdir, args.task, 'sac', args.run_id)
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writer = SummaryWriter(log_path)
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def stop_fn(x):
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return x >= env.spec.reward_threshold
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# trainer
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result = offpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, args.test_num,
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args.batch_size, stop_fn=stop_fn,
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writer=writer, log_interval=args.log_interval)
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assert stop_fn(result['best_reward'])
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train_collector.close()
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test_collector.close()
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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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env = gym.make(args.task)
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collector = Collector(policy, env)
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result = collector.collect(n_episode=1, render=args.render)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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collector.close()
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if __name__ == '__main__':
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__all__ = ('pybullet_envs',) # Avoid F401 error :)
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test_sac()
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3
setup.py
3
setup.py
@ -69,5 +69,8 @@ setup(
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'mujoco': [
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'mujoco_py',
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],
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'pybullet': [
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'pybullet',
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],
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},
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)
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@ -8,7 +8,7 @@ from tianshou.trainer import test_episode, gather_info
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def offpolicy_trainer(policy, train_collector, test_collector, max_epoch,
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step_per_epoch, collect_per_step, episode_per_test,
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batch_size, train_fn=None, test_fn=None, stop_fn=None,
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writer=None, verbose=True, task=''):
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writer=None, log_interval=1, verbose=True, task=''):
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global_step = 0
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best_epoch, best_reward = -1, -1
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stat = {}
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@ -45,7 +45,7 @@ def offpolicy_trainer(policy, train_collector, test_collector, max_epoch,
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losses = policy.learn(train_collector.sample(batch_size))
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for k in result.keys():
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data[k] = f'{result[k]:.2f}'
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if writer:
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if writer and global_step % log_interval == 0:
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writer.add_scalar(
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k + '_' + task if task else k,
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result[k], global_step=global_step)
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@ -54,7 +54,7 @@ def offpolicy_trainer(policy, train_collector, test_collector, max_epoch,
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stat[k] = MovAvg()
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stat[k].add(losses[k])
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data[k] = f'{stat[k].get():.6f}'
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if writer:
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if writer and global_step % log_interval == 0:
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writer.add_scalar(
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k + '_' + task if task else k,
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stat[k].get(), global_step=global_step)
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@ -9,7 +9,7 @@ def onpolicy_trainer(policy, train_collector, test_collector, max_epoch,
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step_per_epoch, collect_per_step, repeat_per_collect,
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episode_per_test, batch_size,
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train_fn=None, test_fn=None, stop_fn=None,
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writer=None, verbose=True, task=''):
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writer=None, log_interval=1, verbose=True, task=''):
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global_step = 0
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best_epoch, best_reward = -1, -1
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stat = {}
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@ -50,7 +50,7 @@ def onpolicy_trainer(policy, train_collector, test_collector, max_epoch,
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global_step += step
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for k in result.keys():
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data[k] = f'{result[k]:.2f}'
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if writer:
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if writer and global_step % log_interval == 0:
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writer.add_scalar(
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k + '_' + task if task else k,
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result[k], global_step=global_step)
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@ -59,7 +59,7 @@ def onpolicy_trainer(policy, train_collector, test_collector, max_epoch,
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stat[k] = MovAvg()
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stat[k].add(losses[k])
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data[k] = f'{stat[k].get():.6f}'
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if writer and global_step:
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if writer and global_step % log_interval == 0:
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writer.add_scalar(
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k + '_' + task if task else k,
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stat[k].get(), global_step=global_step)
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