Reward Shaping via Diffusion Process in Reinforcement Learning
This provides a novel physical perspective on information in RL, though it appears incremental as it builds on existing thermodynamic concepts without demonstrating concrete performance gains.
The paper tackles the exploration-exploitation trade-off in reinforcement learning by developing a reward shaping framework based on diffusion processes and stochastic thermodynamics, resulting in a dual-pronged approach that can be interpreted as either a maximum entropy program or a modified cost optimization program.
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.