Reinforcement Learning under Threats
Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Abstract
The study introduces the concept of Threatened Markov Decision Processes (TMDPs) to address the issue of suboptimal results from Q-learning in reinforcement learning scenarios where adversaries may interfere with the reward generation process.
Rather than viewing the multi-agent system as a game, the focus is on decision-making for a single agent against a potential adversary. Through this model and a proposed level-k thinking scheme, a new learning framework is developed to handle TMDPs, with empirical tests demonstrating the benefits of this opponent modeling approach.
The study introduces Threatened Markov Decision Processes and a level-k reasoning approach for enhanced decision-making in adversarial reinforcement learning scenarios, relevant to cybersecurity, finance, and AI.
Where does it apply?
The Threatened Markov Decision Processes (TMDPs) and level-k reasoning approach are applicable in industries where decision-making occurs in potentially adversarial or competitive environments.
These include, but are not limited to, cybersecurity (for detecting and mitigating threats), finance (for trading strategies in competitive markets), strategic business decisions in competitive industries, and areas of artificial intelligence and machine learning where robust adversarial training is necessary.
Why does it matters?
Threatened Markov Decision Processes (TMDPs) matter because they provide a framework to assist decision-makers in reinforcement learning scenarios where adversarial interference impacts the reward generation process.
The level-k reasoning method for modeling adversarial behavior also offers a novel approach to enhance decision-making under threat models.
Such an approach is crucial in enhancing the robustness and flexibility of AI systems in various fields, including cybersecurity, where the presence of adversarial instances is a significant concern.
Reinforcement Learning under Threats
Proceedings of the AAAI Conference on Artificial Intelligence, 2022
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