# Exploration for Multi-task Reinforcement Learning with Deep Generative Models

arXiv: https://arxiv.org/abs/1611.09894 · HTML: https://mlsanity.com/paper/1611.09894/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2016.

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

- [QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing](https://mlsanity.com/paper/2302.00671/) (2023)

## Abstract (arXiv)

Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as E3, Rmax, Thompson sampling assume a single stationary MDP and are not suitable for system identification in the multi-task setting. We present a novel method to facilitate exploration in multi-task reinforcement learning using deep generative models. We supplement our method with a low dimensional energy model to learn the underlying MDP distribution and provide a resilient and adaptive exploration signal to the agent. We evaluate our method on a new set of environments and provide intuitive interpretation of our results.