A Validation Tool for Designing Reinforcement Learning Environments
- URL: http://arxiv.org/abs/2112.05519v1
- Date: Fri, 10 Dec 2021 13:28:08 GMT
- Title: A Validation Tool for Designing Reinforcement Learning Environments
- Authors: Ruiyang Xu and Zhengxing Chen
- Abstract summary: This study proposes a Markov-based feature analysis method to validate whether an MDP is well formulated.
We believe an MDP suitable for applying RL should contain a set of state features that are both sensitive to actions and predictive in rewards.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reinforcement learning (RL) has gained increasing attraction in the academia
and tech industry with launches to a variety of impactful applications and
products. Although research is being actively conducted on many fronts (e.g.,
offline RL, performance, etc.), many RL practitioners face a challenge that has
been largely ignored: determine whether a designed Markov Decision Process
(MDP) is valid and meaningful. This study proposes a heuristic-based feature
analysis method to validate whether an MDP is well formulated. We believe an
MDP suitable for applying RL should contain a set of state features that are
both sensitive to actions and predictive in rewards. We tested our method in
constructed environments showing that our approach can identify certain invalid
environment formulations. As far as we know, performing validity analysis for
RL problem formulation is a novel direction. We envision that our tool will
serve as a motivational example to help practitioners apply RL in real-world
problems more easily.
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