Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning
Read the original on arXiv Machine Learning →Selective Growing Q‑Networks (SGQN) extend value‑based Q‑learning for continuous control by replacing uniform action‑grid growth with an adaptive grid that adds bins near high‑Q regions and prunes low‑Q bins, while maintaining decoupled Q‑learning and epsilon‑greedy action selection. On eight DeepMind Control Suite tasks, SGQN achieves the highest mean final return on four tasks, matches the best method within 1.1% on three more, and uses on average 7.5 of 9 available bins per dimension. Experiments show that the adaptive grid’s growth and pruning have limited impact on performance, whereas starting from a coarse grid and using epsilon‑greedy selection are critical for success.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.