本文整理汇总了Python中baselines.gail.run_mujoco.runner方法的典型用法代码示例。如果您正苦于以下问题：Python run_mujoco.runner方法的具体用法？Python run_mujoco.runner怎么用？Python run_mujoco.runner使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类baselines.gail.run_mujoco的用法示例。
在下文中一共展示了run_mujoco.runner方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from baselines.gail import run_mujoco [as 别名]
# 或者: from baselines.gail.run_mujoco import runner [as 别名]
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = gym.make(args.env_id)
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2)
env = bench.Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), "monitor.json"))
env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
dataset = Mujoco_Dset(expert_path=args.expert_path, traj_limitation=args.traj_limitation)
savedir_fname = learn(env,
policy_fn,
dataset,
max_iters=args.BC_max_iter,
ckpt_dir=args.checkpoint_dir,
log_dir=args.log_dir,
task_name=task_name,
verbose=True)
avg_len, avg_ret = runner(env,
policy_fn,
savedir_fname,
timesteps_per_batch=1024,
number_trajs=10,
stochastic_policy=args.stochastic_policy,
save=args.save_sample,
reuse=True) 示例2: evaluate_env
# 需要导入模块: from baselines.gail import run_mujoco [as 别名]
# 或者: from baselines.gail.run_mujoco import runner [as 别名]
def evaluate_env(env_name, seed, policy_hidden_size, stochastic, reuse, prefix):
def get_checkpoint_dir(checkpoint_list, limit, prefix):
for checkpoint in checkpoint_list:
if ('limitation_'+str(limit) in checkpoint) and (prefix in checkpoint):
return checkpoint
return None
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
reuse=reuse, hid_size=policy_hidden_size, num_hid_layers=2)
data_path = os.path.join('data', 'deterministic.trpo.' + env_name + '.0.00.npz')
dataset = load_dataset(data_path)
checkpoint_list = glob.glob(os.path.join('checkpoint', '*' + env_name + ".*"))
log = {
'traj_limitation': [],
'upper_bound': [],
'avg_ret': [],
'avg_len': [],
'normalized_ret': []
}
for i, limit in enumerate(CONFIG['traj_limitation']):
# Do one evaluation
upper_bound = sum(dataset.rets[:limit])/limit
checkpoint_dir = get_checkpoint_dir(checkpoint_list, limit, prefix=prefix)
checkpoint_path = tf.train.latest_checkpoint(checkpoint_dir)
env = gym.make(env_name + '-v1')
env.seed(seed)
print('Trajectory limitation: {}, Load checkpoint: {}, '.format(limit, checkpoint_path))
avg_len, avg_ret = run_mujoco.runner(env,
policy_fn,
checkpoint_path,
timesteps_per_batch=1024,
number_trajs=10,
stochastic_policy=stochastic,
reuse=((i != 0) or reuse))
normalized_ret = avg_ret/upper_bound
print('Upper bound: {}, evaluation returns: {}, normalized scores: {}'.format(
upper_bound, avg_ret, normalized_ret))
log['traj_limitation'].append(limit)
log['upper_bound'].append(upper_bound)
log['avg_ret'].append(avg_ret)
log['avg_len'].append(avg_len)
log['normalized_ret'].append(normalized_ret)
env.close()
return log