Bellman Completeness

Bellman completeness is a crucial property in reinforcement learning (RL) that ensures the consistency of value function updates, enabling efficient learning algorithms. Current research focuses on developing computationally efficient algorithms that achieve good performance under this condition, particularly within the context of linear function approximation and offline RL settings, often employing techniques like optimistic value iteration or return-conditioned supervised learning. Addressing the limitations of Bellman completeness, researchers are also exploring alternative assumptions and developing methods that relax this stringent requirement, leading to more robust and broadly applicable RL algorithms. This work has significant implications for improving the sample efficiency and scalability of RL in various applications.