# Dealing with NaNs and infs¶

During the training of a model on a given environment, it is possible that the RL model becomes completely corrupted when a NaN or an inf is given or returned from the RL model.

## How and why?¶

The issue arises then NaNs or infs do not crash, but simply get propagated through the training, until all the floating point number converge to NaN or inf. This is in line with the IEEE Standard for Floating-Point Arithmetic (IEEE 754) standard, as it says:

Note

- Five possible exceptions can occur:
Invalid operation (\(\sqrt{-1}\), \(\inf \times 1\), \(\text{NaN}\ \mathrm{mod}\ 1\), …) return NaN

- Division by zero:
if the operand is not zero (\(1/0\), \(-2/0\), …) returns \(\pm\inf\)

if the operand is zero (\(0/0\)) returns signaling NaN

Overflow (exponent too high to represent) returns \(\pm\inf\)

Underflow (exponent too low to represent) returns \(0\)

Inexact (not representable exactly in base 2, eg: \(1/5\)) returns the rounded value (ex:

`assert (1/5) * 3 == 0.6000000000000001`

)

And of these, only `Division by zero`

will signal an exception, the rest will propagate invalid values quietly.

In python, dividing by zero will indeed raise the exception: `ZeroDivisionError: float division by zero`

,
but ignores the rest.

The default in numpy, will warn: `RuntimeWarning: invalid value encountered`

but will not halt the code.

## Anomaly detection with PyTorch¶

To enable NaN detection in PyTorch you can do

```
import torch as th
th.autograd.set_detect_anomaly(True)
```

## Numpy parameters¶

Numpy has a convenient way of dealing with invalid value: numpy.seterr, which defines for the python process, how it should handle floating point error.

```
import numpy as np
np.seterr(all='raise') # define before your code.
print("numpy test:")
a = np.float64(1.0)
b = np.float64(0.0)
val = a / b # this will now raise an exception instead of a warning.
print(val)
```

but this will also avoid overflow issues on floating point numbers:

```
import numpy as np
np.seterr(all='raise') # define before your code.
print("numpy overflow test:")
a = np.float64(10)
b = np.float64(1000)
val = a ** b # this will now raise an exception
print(val)
```

but will not avoid the propagation issues:

```
import numpy as np
np.seterr(all='raise') # define before your code.
print("numpy propagation test:")
a = np.float64('NaN')
b = np.float64(1.0)
val = a + b # this will neither warn nor raise anything
print(val)
```

## VecCheckNan Wrapper¶

In order to find when and from where the invalid value originated from, stable-baselines3 comes with a `VecCheckNan`

wrapper.

It will monitor the actions, observations, and rewards, indicating what action or observation caused it and from what.

```
import gym
from gym import spaces
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, VecCheckNan
class NanAndInfEnv(gym.Env):
"""Custom Environment that raised NaNs and Infs"""
metadata = {'render.modes': ['human']}
def __init__(self):
super(NanAndInfEnv, self).__init__()
self.action_space = spaces.Box(low=-np.inf, high=np.inf, shape=(1,), dtype=np.float64)
self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(1,), dtype=np.float64)
def step(self, _action):
randf = np.random.rand()
if randf > 0.99:
obs = float('NaN')
elif randf > 0.98:
obs = float('inf')
else:
obs = randf
return [obs], 0.0, False, {}
def reset(self):
return [0.0]
def render(self, mode='human', close=False):
pass
# Create environment
env = DummyVecEnv([lambda: NanAndInfEnv()])
env = VecCheckNan(env, raise_exception=True)
# Instantiate the agent
model = PPO('MlpPolicy', env)
# Train the agent
model.learn(total_timesteps=int(2e5)) # this will crash explaining that the invalid value originated from the environment.
```

## RL Model hyperparameters¶

Depending on your hyperparameters, NaN can occurs much more often. A great example of this: https://github.com/hill-a/stable-baselines/issues/340

Be aware, the hyperparameters given by default seem to work in most cases, however your environment might not play nice with them. If this is the case, try to read up on the effect each hyperparameters has on the model, so that you can try and tune them to get a stable model. Alternatively, you can try automatic hyperparameter tuning (included in the rl zoo).

## Missing values from datasets¶

If your environment is generated from an external dataset, do not forget to make sure your dataset does not contain NaNs. As some datasets will sometimes fill missing values with NaNs as a surrogate value.

Here is some reading material about finding NaNs: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html

And filling the missing values with something else (imputation): https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4