Efficient Meta Reinforcement Learning with Finite Training Tasks using Density Estimation

The core message of this work is to propose a model-based approach for meta reinforcement learning (meta-RL) with a finite set of training tasks. The key idea is to first estimate the prior distribution of tasks using kernel density estimation (KDE), and then train a Bayes-optimal policy with respect to the estimated distribution.