I want to integrate my environment into the OpenAI's gym and then use the stable baselines library for training it.

The learning method in the stable baseline is with one-line learning and you don't have access to the actions that are taken during the training.


More specifically, you don't do the line where you sample from the environment in your code:

action = space.sample()

However, I like to add some logic to the place where I choose the action of the next state and reject some actions that the logic doesn't apply to them (like chess board illegal moves), something like:

for _ in range(1000):
    action = env.space.sample()
    if some_logic(action):

One way to do it is to write a wrapper for the action_space sample() function and only choose legal actions. Like the one here class DiscreteWrapper(spaces.Discrete).

But the problem is that a stable baseline only accepts certain data types and doesn't allow that either.

How can I do it in a way that is integrable into the stable baseline framework and doesn't violate the stable baselines criteria? If that is not possible at all, does anyone know a reinforcement learning framework other that unlike stable baseline allows access to the actions?


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