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In distributed reinforcement learning, it is common to exchange theexperience memory of each agent and thereby collectively train their localmodels. The experience memory, however, contains all the preceding stateobservations and their corresponding policies of the host agent, which mayviolate the privacy of the agent. To avoid this problem, in this work, wepropose a privacy-preserving distributed reinforcement learning (RL) framework,termed federated reinforcement distillation (FRD). The key idea is to exchangea proxy experience memory comprising a pre-arranged set of states andtime-averaged policies, thereby preserving the privacy of actual experiences.Based on an advantage actor-critic RL architecture, we numerically evaluate theeffectiveness of FRD and investigate how the performance of FRD is affected bythe proxy memory structure and different memory exchanging rules.

Federated Reinforcement Distillation with Proxy Experience Memory