Reinforcement Learning (RL) models have continually evolved to navigate the exploration - exploitation trade-off in uncertain Markov Decision Processes (MDPs). In this study, I leverage the principles of stochastic thermodynamics and system dynamics to explore reward shaping via diffusion processes. This provides an elegant framework as a way to think about exploration-exploitation trade-off. This article sheds light on relationships between information entropy, stochastic system dynamics, and their influences on entropy production. This exploration allows us to construct a dual-pronged framework that can be interpreted as either a maximum entropy program for deriving efficient policies or a modified cost optimization program accounting for informational costs and benefits. This work presents a novel perspective on the physical nature of information and its implications for online learning in MDPs, consequently providing a better understanding of information-oriented formulations in RL.

翻译：强化学习（RL）模型在不断演进，以应对不确定的马尔可夫决策过程（MDP）中的探索与利用权衡。在本研究中，我利用随机热力学和系统动力学原理，探索通过扩散过程进行奖励塑造的方法，这为思考探索-利用权衡提供了一种优雅的框架。本文揭示了信息熵、随机系统动力学及其对熵产生的影响之间的关系。这一探索使我们能够构建一个双管齐下的框架，该框架既可解释为用于推导高效策略的最大熵程序，也可解释为考虑信息成本与收益的修正成本优化程序。本工作为信息的物理本质及其在MDP在线学习中的影响提供了新颖视角，从而更深入地理解了RL中以信息为导向的表述形式。