Unified Framework for Dynamic Reward Shaping in Reinforcement Learning
A new paper on arXiv (2608.08158) presents an in-depth framework for dynamic reward shaping in reinforcement learning, addressing challenges like sparse, delayed, and low-quality rewards. The researchers note that while traditional potential-based reward shaping maintains safety with fixed signals, there's a growing need for flexible approaches as both the learner and the surrounding information evolve. Their framework includes various adaptive reward strategies, such as exploration, Bayesian inference, human-in-the-loop learning, automated reward design, and methods based on foundation models. The paper has already been submitted and is available at the provided link.
Key facts
- The paper is titled 'A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning'.
- It is published on arXiv with identifier 2608.08158.
- The announcement type is 'new'.
- The paper addresses sparse, delayed, and weakly informative rewards in reinforcement learning.
- It discusses potential-based reward shaping and its safety guarantees for fixed signals.
- It highlights the need for adaptive reward mechanisms in contemporary systems.
- The framework covers exploration, Bayesian inference, human-in-the-loop learning, automated reward design, and foundation-model-based approaches.
- The source URL is https://arxiv.org/abs/2608.08158.
Entities
Institutions
- arXiv