Source code for stable_baselines3.common.vec_env.vec_check_nan

import warnings
from typing import List, Tuple

import numpy as np
from gym 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)