The content discusses the application of slowly changing adversarial bandit algorithms in discounted Markov Decision Processes (MDPs). It explores a reduction from reinforcement learning to bandits, addressing challenges such as objective mismatch and sticky bandits. The analysis includes related works, assumptions, and a case study on EXP3 algorithm.
The work highlights the importance of assumptions for exploration in MDPs and suggests potential improvements to reduce dependencies on parameters like state space size and effective horizon. Future directions include exploring stochastic bandit algorithms and extending the framework to function approximation implementations.
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by Ian A. Kash,... às arxiv.org 03-12-2024
https://arxiv.org/pdf/2205.09056.pdfPerguntas Mais Profundas