変化する疎報酬環境に適応する分散調整法
Adaptive Variance for Changing Sparse-Reward Environments
価値関数と最適探索の関係を理論的に解析し、ガウス方策の分散を適応的に調整することで、環境変化に素早く適応できる手法を提案した。
著者: Xingyu Lin, Pengsheng Guo, Carlos Florensa, David Held
分類: cs.RO, cs.AI
原文アブストラクト
Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration. We propose a principled way to adapt the policy for better exploration in changing sparse-reward environments. Unlike previous works which explicitly model environmental changes, we analyze the relationship between the value function and the optimal exploration for a Gaussian-parameterized policy and show that our theory leads to an effective strategy for adjusting the variance of the policy, enabling fast adapt to changes in a variety of sparse-reward environments.
