import warnings
from typing import List, Tuple
import numpy as np
from gymnasium import spaces
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs, VecEnvStepReturn, VecEnvWrapper
[docs]class VecCheckNan(VecEnvWrapper):
"""
NaN and inf checking wrapper for vectorized environment, will raise a warning by default,
allowing you to know from what the NaN of inf originated from.
:param venv: the vectorized environment to wrap
:param raise_exception: Whether to raise a ValueError, instead of a UserWarning
:param warn_once: Whether to only warn once.
:param check_inf: Whether to check for +inf or -inf as well
"""
def __init__(self, venv: VecEnv, raise_exception: bool = False, warn_once: bool = True, check_inf: bool = True) -> None:
super().__init__(venv)
self.raise_exception = raise_exception
self.warn_once = warn_once
self.check_inf = check_inf
self._user_warned = False
self._actions: np.ndarray
self._observations: VecEnvObs
if isinstance(venv.action_space, spaces.Dict):
raise NotImplementedError("VecCheckNan doesn't support dict action spaces")
[docs] def step_async(self, actions: np.ndarray) -> None:
self._check_val(event="step_async", actions=actions)
self._actions = actions
self.venv.step_async(actions)
[docs] def step_wait(self) -> VecEnvStepReturn:
observations, rewards, dones, infos = self.venv.step_wait()
self._check_val(event="step_wait", observations=observations, rewards=rewards, dones=dones)
self._observations = observations
return observations, rewards, dones, infos
[docs] def reset(self) -> VecEnvObs:
observations = self.venv.reset()
self._check_val(event="reset", observations=observations)
self._observations = observations
return observations
[docs] def check_array_value(self, name: str, value: np.ndarray) -> List[Tuple[str, str]]:
"""
Check for inf and NaN for a single numpy array.
:param name: Name of the value being check
:param value: Value (numpy array) to check
:return: A list of issues found.
"""
found = []
has_nan = np.any(np.isnan(value))
has_inf = self.check_inf and np.any(np.isinf(value))
if has_inf:
found.append((name, "inf"))
if has_nan:
found.append((name, "nan"))
return found
def _check_val(self, event: str, **kwargs) -> None:
# if warn and warn once and have warned once: then stop checking
if not self.raise_exception and self.warn_once and self._user_warned:
return
found = []
for name, value in kwargs.items():
if isinstance(value, (np.ndarray, list)):
found += self.check_array_value(name, np.asarray(value))
elif isinstance(value, dict):
for inner_name, inner_val in value.items():
found += self.check_array_value(f"{name}.{inner_name}", inner_val)
elif isinstance(value, tuple):
for idx, inner_val in enumerate(value):
found += self.check_array_value(f"{name}.{idx}", inner_val)
else:
raise TypeError(f"Unsupported observation type {type(value)}.")
if found:
self._user_warned = True
msg = ""
for i, (name, type_val) in enumerate(found):
msg += f"found {type_val} in {name}"
if i != len(found) - 1:
msg += ", "
msg += ".\r\nOriginated from the "
if event == "reset":
msg += "environment observation (at reset)"
elif event == "step_wait":
msg += f"environment, Last given value was: \r\n\taction={self._actions}"
elif event == "step_async":
msg += f"RL model, Last given value was: \r\n\tobservations={self._observations}"
else:
raise ValueError("Internal error.")
if self.raise_exception:
raise ValueError(msg)
else:
warnings.warn(msg, UserWarning)