# Federated Reinforcement Distillation with Proxy Experience Memory

## TL;DR

This paper introduces a privacy-preserving federated reinforcement learning framework that exchanges proxy experience memories instead of actual experiences, maintaining privacy while enabling distributed training.

## Contribution

It proposes a novel federated reinforcement distillation method using proxy experience memory to protect privacy in distributed RL.

## Key findings

- FRD effectively trains agents with privacy preservation.
- Performance depends on proxy memory structure and exchange rules.
- Numerical evaluations demonstrate FRD's viability.

## Abstract

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.