Feb. 9, 2024, 5:44 a.m. | Yanran Wang David Boyle

cs.LG updates on arXiv.org arxiv.org

Reinforcement Learning or optimal control can provide effective reasoning for sequential decision-making problems with variable dynamics. Such reasoning in practical implementation, however, poses a persistent challenge in interpreting the reward function and corresponding optimal policy. Consequently, formalizing the sequential decision-making problems as inference has a considerable value, as probabilistic inference in principle offers diverse and powerful mathematical tools to infer the stochastic dynamics whilst suggesting a probabilistic interpretation of the reward design and policy convergence. In this study, we propose …

challenge control cs.ai cs.lg cs.ro cs.sy decision dynamics eess.sy function implementation inference interpretability making policy practical reasoning reinforcement reinforcement learning value

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