Robust Reward Machine Learning and Execution in Noisy Environments

arXiv:2611.01063v1 Announce Type: new Abstract: Background: Reward machines (RMs) represent non-Markovian reward functions as finite-state machines over high-level propositional events. Traditionally, these events are generated by a deterministic labelling function that maps environment states to proposition sets. However, real-world labelling functions are inherently noisy. Objectives: Design and implement a method that learns RMs in the presence of noisy labelling functions and exploits them for policy learning in both discrete and continuous environments. Methods: We propose Prob-IRM, a method that learns RMs from noisy proposition histories using inductive logic programming (ILP) and exploits them for policy learning. Noisy proposition histories are represented as training examples for ILP, which learns an RM that maximises coverage of low-uncertainty histories. RM learning is interleaved with policy optimisation. Prob-IRM uses a belief over RM states to determine when to relearn the RM and to provide additional guiding rewards. Results: Prob-IRM is evaluated on environments with discrete and continuous state spaces under varying levels of labelling noise. Prob-IRM performs comparably to baselines using handcrafted RMs and outperforms approaches that use recurrent neural networks to encode history, despite operating with noisy labels. Conclusions: Prob-IRM represents a step towards deploying reward machines in noisy, real-world environments.

Abstract:Background: Reward machines (RMs) represent non-Markovian reward functions as finite-state machines over high-level propositional events. Traditionally, these events are generated by a deterministic labelling function that maps environment states to proposition sets. However, real-world labelling functions are inherently noisy. Objectives: Design and implement a method that learns RMs in the presence of noisy labelling functions and exploits them for policy learning in both discrete and continuous environments. Methods: We propose Prob-IRM, a method that learns RMs from noisy proposition histories using inductive logic programming (ILP) and exploits them for policy learning. Noisy proposition histories are represented as training examples for ILP, which learns an RM that maximises coverage of low-uncertainty histories. RM learning is interleaved with policy optimisation. Prob-IRM uses a belief over RM states to determine when to relearn the RM and to provide additional guiding rewards. Results: Prob-IRM is evaluated on environments with discrete and continuous state spaces under varying levels of labelling noise. Prob-IRM performs comparably to baselines using handcrafted RMs and outperforms approaches that use recurrent neural networks to encode history, despite operating with noisy labels. Conclusions: Prob-IRM represents a step towards deploying reward machines in noisy, real-world environments.

44 pages, 21 figures, 5 tables. Published in the Journal of Artificial Intelligence Research. Code: this https URL

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2611.01063 [cs.AI]

(or arXiv:2611.01063v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.01063 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1613/jair.1.22539 Focus to learn more DOI(s) linking to related resources

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Robust Reward Machine Learning and Execution in Noisy Environments

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