学習可能なフーリエ特徴による強化学習の関数正則化
Functional Regularization for Reinforcement Learning via Learned Fourier Features
入力を学習可能なフーリエ基底に埋め込むことで、状態ベースと画像ベース両方の強化学習のサンプル効率を改善する手法を提案し、NTK解析で初期分散の調整が関数正則化に相当することを示した。
著者: Alexander C. Li, Deepak Pathak
分類: cs.LG, cs.AI, cs.CV, cs.NE, cs.RO
原文アブストラクト
We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis and show that it improves the sample efficiency of both state-based and image-based RL. We perform infinite-width analysis of our architecture using the Neural Tangent Kernel and theoretically show that tuning the initial variance of the Fourier basis is equivalent to functional regularization of the learned deep network. That is, these learned Fourier features allow for adjusting the degree to which networks underfit or overfit different frequencies in the training data, and hence provide a controlled mechanism to improve the stability and performance of RL optimization. Empirically, this allows us to prioritize learning low-frequency functions and speed up learning by reducing networks' susceptibility to noise in the optimization process, such as during Bellman updates. Experiments on standard state-based and image-based RL benchmarks show clear benefits of our architecture over the baselines. Website at https://alexanderli.com/learned-fourier-features
