Optimistic reinforcement learning by forward Kullback–Leibler divergence optimization

This paper addresses a new interpretation of the traditional optimization method in reinforcement learning (RL) as optimization problems using reverse Kullback–Leibler (KL) divergence, and derives a new optimization method using forward KL divergence, instead of reverse KL divergence in the optimiza...

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Veröffentlicht in:	Neural networks 2022-08, Vol.152, p.169-180
1. Verfasser:	Kobayashi, Taisuke
Format:	Artikel
Sprache:	eng
Schlagworte:	Control as probabilistic inference Kullback–Leibler divergence Optimistic learning Reinforcement learning
Online-Zugang:	Volltext
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