A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning

arXiv:2112.15400v4 Announce Type: replace
Abstract: Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforcement learner to achieve fast adaptations. In this paper, we develop a unified framework that describes variations of GMRL algorithms and points out that existing stochastic meta-gradient estimators adopted by GMRL are actually \textbf{biased}. Such meta-gradient bias comes from two sources: 1) the compositional bias incurred by the two-level problem structure, which has an upper bound …

abstract algorithms arxiv bias cs.ai cs.lg framework gradient guides loop meta optimisation paper reinforcement reinforcement learning stochastic type understanding