視覚からエンパワーメントを効率的に推定する潜在表現学習
Learning Efficient Representation for Intrinsic Motivation
視覚観測のみから未知のダイナミクスにおけるエンパワーメントを、サンプリング不要で効率的に計算できる潜在空間の確率的モデルを提案した論文。
著者: Ruihan Zhao, Stas Tiomkin, Pieter Abbeel
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Mutual Information between agent Actions and environment States (MIAS) quantifies the influence of agent on its environment. Recently, it was found that the maximization of MIAS can be used as an intrinsic motivation for artificial agents. In literature, the term empowerment is used to represent the maximum of MIAS at a certain state. While empowerment has been shown to solve a broad range of reinforcement learning problems, its calculation in arbitrary dynamics is a challenging problem because it relies on the estimation of mutual information. Existing approaches, which rely on sampling, are limited to low dimensional spaces, because high-confidence distribution-free lower bounds for mutual information require exponential number of samples. In this work, we develop a novel approach for the estimation of empowerment in unknown dynamics from visual observation only, without the need to sample for MIAS. The core idea is to represent the relation between action sequences and future states using a stochastic dynamic model in latent space with a specific form. This allows us to efficiently compute empowerment with the "Water-Filling" algorithm from information theory. We construct this embedding with deep neural networks trained on a sophisticated objective function. Our experimental results show that the designed embedding preserves information-theoretic properties of the original dynamics.
