Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation Approach

arXiv:2206.10716v2 Announce Type: replace
Abstract: In meta reinforcement learning (meta RL), an agent learns from a set of training tasks how to quickly solve a new task, drawn from the same task distribution. The optimal meta RL policy, a.k.a. the Bayes-optimal behavior, is well defined, and guarantees optimal reward in expectation, taken with respect to the task distribution. The question we explore in this work is how many training tasks are required to guarantee approximately optimal behavior with high probability. …

abstract agent arxiv bayes behavior cs.ai cs.lg distribution meta policy reinforcement reinforcement learning set solve tasks training type