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This research introduces Threatened Markov Decision Processes (TMDPs) as a formal framework for reinforcement learning agents to detect and counter adversaries manipulating reward signals. By deriving theoretical results and validating them through extensive experiments, the study demonstrates that adversarial awareness is critical for maintaining agent integrity in competitive environments. This work advances the field of robust machine learning by providing novel schemes to handle interference within RL contexts.
Opponent Aware Reinforcement Learning
arXiv:1908.08773v3 Announce Type: replace Abstract: In certain reinforcement learning (RL) scenarios there are adversaries trying to interfere with the underlying reward process for their own benefit. We introduce Threatened Markov Decision Processes (TMDPs) as a framework to support an agent against potential opponents in an RL context as well as schemes resulting in novel learning approaches to deal with TMDPs. After introducing our framework and deriving theoretical results, empirical evidence is given via extensive experiments, showing the importance for an RL agent of acknowledging adversarial awareness.
Abstract:In certain reinforcement learning (RL) scenarios there are adversaries trying to interfere with the underlying reward process for their own benefit. We introduce Threatened Markov Decision Processes (TMDPs) as a framework to support an agent against potential opponents in an RL context as well as schemes resulting in novel learning approaches to deal with TMDPs. After introducing our framework and deriving theoretical results, empirical evidence is given via extensive experiments, showing the importance for an RL agent of acknowledging adversarial awareness.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:1908.08773 [cs.LG]
(or arXiv:1908.08773v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.1908.08773 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1016/j.ejor.2026.08.031 Focus to learn more DOI(s) linking to related resources
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