進化的方策最適化
Evolutionary Policy Optimization
進化的アルゴリズムのスケーラビリティと方策勾配の安定性を組み合わせたハイブリッド強化学習手法EPOを提案し、巧みな操作や歩行タスクで性能を向上させた。
著者: Jianren Wang, Yifan Su, Abhinav Gupta, Deepak Pathak
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
On-policy reinforcement learning (RL) algorithms are widely used for their strong asymptotic performance and training stability, but they struggle to scale with larger batch sizes, as additional parallel environments yield redundant data due to limited policy-induced diversity. In contrast, Evolutionary Algorithms (EAs) scale naturally and encourage exploration via randomized population-based search, but are often sample-inefficient. We propose Evolutionary Policy Optimization (EPO), a hybrid algorithm that combines the scalability and diversity of EAs with the performance and stability of policy gradients. EPO maintains a population of agents conditioned on latent variables, shares actor-critic network parameters for coherence and memory efficiency, and aggregates diverse experiences into a master agent. Across tasks in dexterous manipulation, legged locomotion, and classic control, EPO outperforms state-of-the-art baselines in sample efficiency, asymptotic performance, and scalability.
