Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation Approach. (arXiv:2206.10716v1 [cs.LG])

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. Recent work provided the first …

arxiv learning lg meta reinforcement reinforcement learning training