Meta-Q-Learning
Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola
Abstract
This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL). MQL builds upon three simple ideas. First, we show that Q-learning is competitive with state-of-the-art meta-RL algorithms if given access to a context variable that is a representation of the past trajectory. Second, a multi-task objective to maximize the average reward across the training tasks is an effective method to meta-train RL policies. Third, past data from the meta-training replay buffer can be recycled to adapt the policy on a new task using off-policy updates. MQL draws upon ideas in propensity estimation to do so and thereby amplifies the amount of available data for adaptation. Experiments on standard continuous-control benchmarks suggest that MQL compares favorably with the state of the art in meta-RL.
BibTeX
@inproceedings{
Fakoor2020Meta-Q-Learning,
title={Meta-Q-Learning},
author={Rasool Fakoor and Pratik Chaudhari and Stefano Soatto and Alexander J. Smola},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SJeD3CEFPH}
}