Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research
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2025
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Abstract
Reinforcement learning (RL) provides a powerful method to address problems in
operations research. However, its real-world application often fails due to a
lack of user acceptance and trust. A possible remedy is to provide managers
with the possibility of altering the RL policy by incorporating human expert
knowledge. In this study, we analyze the benefits and caveats of including
human knowledge via action masking. While action masking has so far been used
to exclude invalid actions, its ability to integrate human expertise remains
underexplored. Human knowledge is often encapsulated in heuristics, which
suggest reasonable, near-optimal actions in certain situations. Enforcing such
actions should hence increase trust among the human workforce to rely on the
model's decisions. Yet, a strict enforcement of heuristic actions may also
restrict the policy from exploring superior actions, thereby leading to overall
lower performance. We analyze the effects of action masking based on three
problems with different characteristics, namely, paint shop scheduling, peak
load management, and inventory management. Our findings demonstrate that
incorporating human knowledge through action masking can achieve substantial
improvements over policies trained without action masking. In addition, we find
that action masking is crucial for learning effective policies in constrained
action spaces, where certain actions can only be performed a limited number of
times. Finally, we highlight the potential for suboptimal outcomes when action
masks are overly restrictive.
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| Authors | Mirko Stappert; Bernhard Lutz; Niklas Goby; Dirk Neumann |
| Journal | arXiv |
| Year | 2025 |
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