Core Concepts
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.
Abstract
The authors address the problem of meta reinforcement learning (meta-RL), where an agent learns from a set of training tasks how to quickly solve a new task drawn from the same task distribution. They propose a model-based approach that consists of two main steps:
Density Estimation: The authors use kernel density estimation (KDE) to learn an estimate of the prior distribution over the task parameters from the finite set of training tasks.
Policy Optimization: The authors then train a Bayes-optimal policy with respect to the estimated task distribution, instead of the true but unknown prior.
The key advantage of this approach is that it can exploit the low-dimensional structure of the task distribution, if such structure exists. The authors provide PAC-style generalization bounds that show the regret of the learned policy scales exponentially with the dimension of the task distribution, rather than the number of states and actions as in previous work.
The authors also demonstrate the practical potential of their approach by incorporating it into the state-of-the-art VariBAD meta-RL algorithm. They show that using the KDE-estimated task distribution to generate "dream" environments for training can improve the generalization performance of VariBAD, especially when the number of training tasks is small.
Stats
The authors do not provide any specific numerical data or statistics in the content. The content focuses on the theoretical analysis and high-level description of the proposed approach.
Quotes
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