Reward Shaping via Diffusion Process in Reinforcement Learning

• URL: http://arxiv.org/abs/2306.11885v1
• Date: Tue, 20 Jun 2023 20:58:33 GMT
• Title: Reward Shaping via Diffusion Process in Reinforcement Learning
• Authors: Peeyush Kumar
• Abstract summary: I leverage the principles of thermodynamics and system dynamics to explore reward shaping via diffusion processes. This article sheds light on relationships between information entropy, system dynamics, and their influences on entropy production.
• Score: 1.0878040851638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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