# Multi-Task Policy Search

arXiv: https://arxiv.org/abs/1307.0813 · HTML: https://mlsanity.com/paper/1307.0813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2013.

## Built on by (most cited first, 1 of 1)

- [Lifetime policy reuse and the importance of task capacity](https://mlsanity.com/paper/2106.01741/) (2021)

## Abstract (arXiv)

Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in real-robot experiments are shown.