# Factored Contextual Policy Search with Bayesian Optimization

## TL;DR

This paper introduces a structured approach to contextual policy search by factoring contexts into target and environment components, enhancing data efficiency and generalization in robotic learning through Bayesian optimization.

## Contribution

It proposes a novel factorization of context variables in Bayesian optimization-based policy search, improving learning speed and generalization in complex robotic tasks.

## Key findings

- Faster learning in robotic simulations.
- Improved generalization across different task contexts.
- Effective application of factorization in Bayesian optimization.

## Abstract

Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitly conditioning the policy on a parametric context space. In this paper, we further structure the contextual policy representation. We propose to factor contexts into two components: target contexts that describe the task objectives, e.g. target position for throwing a ball; and environment contexts that characterize the environment, e.g. initial position or mass of the ball. Our key observation is that experience can be directly generalized over target contexts. We show that this can be easily exploited in contextual policy search algorithms. In particular, we apply factorization to a Bayesian optimization approach to contextual policy search both in sampling-based and active learning settings. Our simulation results show faster learning and better generalization in various robotic domains. See our supplementary video: https://youtu.be/MNTbBAOufDY.