Value Expansion

Value expansion methods aim to improve reinforcement learning algorithms by generating more accurate value function estimates through iterative model-based predictions. Current research focuses on understanding the limitations of these methods, particularly the compounding errors in model-based approaches and the diminishing returns of longer prediction horizons. While model-free alternatives sometimes match or exceed the performance of model-based methods, ongoing work explores techniques like Bayesian filtering and conservative model-based approaches to mitigate these issues and improve sample efficiency in offline reinforcement learning. These advancements have significant implications for improving the efficiency and robustness of reinforcement learning algorithms across various applications.