本文整理汇总了Python中utils.make_env方法的典型用法代码示例。如果您正苦于以下问题：Python utils.make_env方法的具体用法？Python utils.make_env怎么用？Python utils.make_env使用的例子？那么, 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utils的用法示例。
在下文中一共展示了utils.make_env方法的4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: eval_policy
# 需要导入模块: import utils [as 别名]
# 或者: from utils import make_env [as 别名]
def eval_policy(policy, env_name, seed, eval_episodes=10):
eval_env, _, _, _ = utils.make_env(env_name, atari_preprocessing)
eval_env.seed(seed + 100)
avg_reward = 0.
for _ in range(eval_episodes):
state, done = eval_env.reset(), False
while not done:
action = policy.select_action(np.array(state), eval=True)
state, reward, done, _ = eval_env.step(action)
avg_reward += reward
avg_reward /= eval_episodes
print("---------------------------------------")
print(f"Evaluation over {eval_episodes} episodes: {avg_reward:.3f}")
print("---------------------------------------")
return avg_reward 示例2: make_envs
# 需要导入模块: import utils [as 别名]
# 或者: from utils import make_env [as 别名]
def make_envs(env_id, n_envs, seed):
def wrap_make_env(env_id, rank):
def _thunk():
return make_env(env_id, seed + rank)
return _thunk
set_global_seeds(seed)
env = SubprocVecEnv(env_id, [wrap_make_env(env_id, i)
for i in range(n_envs)])
return env 示例3: main
# 需要导入模块: import utils [as 别名]
# 或者: from utils import make_env [as 别名]
def main():
args = parse_args()
env = make_env(args.env)
model = get_model(args.policy_ckpt_dir)
if args.reward_predictor_ckpt_dir:
reward_predictor = get_reward_predictor(args.reward_predictor_ckpt_dir)
else:
reward_predictor = None
run_agent(env, model, reward_predictor, args.frame_interval_ms) 示例4: create_env
# 需要导入模块: import utils [as 别名]
# 或者: from utils import make_env [as 别名]
def create_env(n_envs, eval_env=False):
"""
Create the environment and wrap it if necessary
:param n_envs: (int)
:param eval_env: (bool) Whether is it an environment used for evaluation or not
:return: (Union[gym.Env, VecEnv])
:return: (gym.Env)
"""
global hyperparams
global env_kwargs
# Do not log eval env (issue with writing the same file)
log_dir = None if eval_env else save_path
if is_atari:
if args.verbose > 0:
print("Using Atari wrapper")
env = make_atari_env(env_id, num_env=n_envs, seed=args.seed)
# Frame-stacking with 4 frames
env = VecFrameStack(env, n_stack=4)
elif algo_ in ['dqn', 'ddpg']:
if hyperparams.get('normalize', False):
print("WARNING: normalization not supported yet for DDPG/DQN")
env = gym.make(env_id, **env_kwargs)
env.seed(args.seed)
if env_wrapper is not None:
env = env_wrapper(env)
else:
if n_envs == 1:
env = DummyVecEnv([make_env(env_id, 0, args.seed, wrapper_class=env_wrapper, log_dir=log_dir, env_kwargs=env_kwargs)])
else:
# env = SubprocVecEnv([make_env(env_id, i, args.seed) for i in range(n_envs)])
# On most env, SubprocVecEnv does not help and is quite memory hungry
env = DummyVecEnv([make_env(env_id, i, args.seed, log_dir=log_dir,
wrapper_class=env_wrapper, env_kwargs=env_kwargs) for i in range(n_envs)])
if normalize:
if args.verbose > 0:
if len(normalize_kwargs) > 0:
print("Normalization activated: {}".format(normalize_kwargs))
else:
print("Normalizing input and reward")
env = VecNormalize(env, **normalize_kwargs)
# Optional Frame-stacking
if hyperparams.get('frame_stack', False):
n_stack = hyperparams['frame_stack']
env = VecFrameStack(env, n_stack)
print("Stacking {} frames".format(n_stack))
del hyperparams['frame_stack']
if args.algo == 'her':
# Wrap the env if need to flatten the dict obs
if isinstance(env, VecEnv):
env = _UnvecWrapper(env)
env = HERGoalEnvWrapper(env)
return env