Exploring Action Quantization for Data-Driven Learning in Robotic Continuous Control Tasks

This paper explores the effectiveness of state-of-the-art action quantization techniques in enhancing offline policy learning for robotic continuous control tasks. We compare three methods—AQuaDem, SAQ, and CEIL—and find that while action quantization improves learning performance, the optimal technique depends on the specific environment and dataset characteristics. Our results highlight the importance of selecting the appropriate method based on these factors.