Abstract
Reinforcement Learning (RL) needs sufficient exploration to learn an optimal policy. However, exploratory actions could lead the learning agent to safety hazards, not necessarily in the next state but in the future. Therefore, it is essential to evaluate each action beforehand to ensure safety. The exploratory actions and the actions proposed by the RL agent could also be unsafe during training and in the deployment phase. In this work, we have proposed the Imperative Action Masking Framework, a Graph-Plan-based method considering a finite and small look ahead to assess the safety of actions from the current state. This information is used to construct action masks on the run, filtering out the unsafe actions proposed by the RL agent (including the exploitative ones). The Graph-Plan-based method makes our framework interpretable, while the finite and small look ahead makes the proposed method scalable for larger environments. However, considering the finite and small look ahead comes with a cost of overlooking safety beyond the look ahead. We have done a comparative study against the probabilistic safety shield in Pacman and Warehouse environments approach. Our framework has produced better results in terms of both safety and reward.
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Dey, S., Bhat, S., Dasgupta, P., Dey, S. (2023). Imperative Action Masking for Safe Exploration in Reinforcement Learning. In: Calvaresi, D., et al. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2023. Lecture Notes in Computer Science(), vol 14127. Springer, Cham. https://doi.org/10.1007/978-3-031-40878-6_8
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DOI: https://doi.org/10.1007/978-3-031-40878-6_8
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