text: 459,324,1313747256957263874,1189826682263179265,Yuji Kanagawa,"Exploration is generally difficult, but if there are only a few rewarding states, we can use a simple inductive bias: just visit diverse regions! We propose IMOC, an option-learning method that learns diverse options. paper: <LINK> code: <LINK> <LINK>",https://arxiv.org/abs/2010.02756,"In this paper, we study the problem of autonomously discovering temporally abstracted actions, or options, for exploration in reinforcement learning. For learning diverse options suitable for exploration, we introduce the infomax termination objective defined as the mutual information between options and their corresponding state transitions. We derive a scalable optimization scheme for maximizing this objective via the termination condition of options, yielding the InfoMax Option Critic (IMOC) algorithm. Through illustrative experiments, we empirically show that IMOC learns diverse options and utilizes them for exploration. Moreover, we show that IMOC scales well to continuous control tasks. ",Diverse Exploration via InfoMax Options,1,"['Exploration is generally difficult, but if there are only a few rewarding states, we can use a simple inductive bias: just visit diverse regions!\nWe propose IMOC,  an option-learning method that learns diverse options.\npaper: <LINK>\ncode: <LINK> <LINK>']",20,10,251