Learning to share and hide intentions using information regularization
Name
8227-learning-to-share-and-hide-intentions-using-information-regularization.pdf
Description
Published version
Size
4.6 MB
Format
Adobe PDF
Checksum (MD5)
9c77a90a8881a189546fe492a67a00fa
Author(s)
Kleiman-Weiner, Max
Tenenbaum, Joshua B
Date Issued
December 2018
Journal
32nd Conference on Neural Information Processing Systems (NeurIPS 2018)
Publisher
Curran Associates
Citation
Strouse, D. J. et al. “.” Paper presented at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Dec 3-8 2018, Curran Associates © 2018 The Author(s)
Version
Final published version
Abstract
Learning to cooperate with friends and compete with foes is a key component of multi-agent reinforcement learning. Typically to do so, one requires access to either a model of or interaction with the other agent(s). Here we show how to learn effective strategies for cooperation and competition in an asymmetric information game with no such model or interaction. Our approach is to encourage an agent to reveal or hide their intentions using an information-theoretic regularizer. We consider both the mutual information between goal and action given state, as well as the mutual information between goal and state. We show how to optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning. Finally, we demonstrate that cooperative (competitive) policies learned with our approach lead to more (less) reward for a second agent in two simple asymmetric information games.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/8227-learning-to-share-and-hide-intentions-using-information-regularization
