mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-14 20:58:03 +00:00
daaebd0a52
* Drop python 3.8 support, add python 3.12 support * Upgrade to python 3.9 syntax * Fixes for Numpy v2 * Fix doc warning
107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
import warnings
|
|
|
|
import numpy as np
|
|
from gymnasium import spaces
|
|
|
|
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs, VecEnvStepReturn, VecEnvWrapper
|
|
|
|
|
|
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")
|
|
|
|
def step_async(self, actions: np.ndarray) -> None:
|
|
self._check_val(event="step_async", actions=actions)
|
|
self._actions = actions
|
|
self.venv.step_async(actions)
|
|
|
|
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
|
|
|
|
def reset(self) -> VecEnvObs:
|
|
observations = self.venv.reset()
|
|
self._check_val(event="reset", observations=observations)
|
|
self._observations = observations
|
|
return observations
|
|
|
|
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)
|