Chunk-Guided Q-Learning

Researchers introduce Chunk-Guided Q-Learning (CGQ), a new offline reinforcement learning algorithm that combines single-step and multi-step temporal difference learning approaches. The method achieves better performance on long-horizon tasks by reducing error accumulation while maintaining fine-grained value propagation, with theoretical guarantees and empirical validation on OGBench tasks.