In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models. The experience memory, however, contains all the preceding state observations and their corresponding policies of the host agent, which may violate the privacy of the agent. To avoid this problem, in this work, we propose a privacy-preserving distributed reinforcement learning (RL) framework, termed federated reinforcement distillation (FRD). The key idea is to exchange a proxy experience memory comprising a pre-arranged set of states and time-averaged policies, thereby preserving the privacy of actual experiences. Based on an advantage actor-critic RL architecture, we numerically evaluate the effectiveness of FRD and investigate how the performance of FRD is affected by the proxy memory structure and different memory exchanging rules.
@article{arxiv.1907.06536,
title = {Federated Reinforcement Distillation with Proxy Experience Memory},
author = {Han Cha and Jihong Park and Hyesung Kim and Seong-Lyun Kim and Mehdi Bennis},
journal= {arXiv preprint arXiv:1907.06536},
year = {2020}
}
Comments
To be presented at the 28th International Joint Conference on Artificial Intelligence (IJCAI-19), 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML'19), Macao, China