Wasserstein・Sinkhorn信頼領域による方策最適化の収束保証
Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
KLダイバージェンスの代わりにWasserstein距離とSinkhorn距離を信頼領域に用いた方策最適化を提案し、大域的最適性への収束を理論的に示すとともに、ロボット歩行や連続制御タスクで性能向上を実証した。
著者: Jun Song, Niao He, Lijun Ding, Chaoyue Zhao
分類: cs.LG, cs.AI, math.OC
原文アブストラクト
Trust-region methods based on Kullback-Leibler divergence are pervasively used to stabilize policy optimization in reinforcement learning. In this paper, we exploit more flexible metrics and examine two natural extensions of policy optimization with Wasserstein and Sinkhorn trust regions, namely Wasserstein policy optimization (WPO) and Sinkhorn policy optimization (SPO). Instead of restricting the policy to a parametric distribution class, we directly optimize the policy distribution and derive their closed-form policy updates based on the Lagrangian duality. Theoretically, we show that WPO guarantees a monotonic performance improvement, and SPO provably converges to WPO as the entropic regularizer diminishes. Moreover, we prove that with a decaying Lagrangian multiplier to the trust region constraint, both methods converge to global optimality. Experiments across tabular domains, robotic locomotion, and continuous control tasks further demonstrate the performance improvement of both approaches, more robustness of WPO to sample insufficiency, and faster convergence of SPO, over state-of-art policy gradient methods.