Diverse Exploration for Fast and Safe Policy Improvement

Andrew Cohen, Lei Yu, Robert Wright
2018 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration strategy - diverse exploration (DE), which learns and deploys a diverse set of safe policies to explore the environment. We provide DE theory explaining why diversity in behavior policies enables effective exploration without sacrificing exploitation. Our empirical study shows that an online policy improvement
more » ... hm framework implementing the DE strategy can achieve both fast policy improvement and safe online performance.
doi:10.1609/aaai.v32i1.11758 fatcat:rab4rdrvajafhnek6x3kvd6nj4