Federated Reinforcement Distillation with Proxy Experience Memory
This addresses privacy protection for agents in distributed reinforcement learning systems, though it appears incremental as it builds on existing advantage actor-critic architectures.
The paper tackles privacy concerns in distributed reinforcement learning by proposing Federated Reinforcement Distillation (FRD), which exchanges proxy experience memory instead of actual experiences, achieving competitive performance with existing methods while preserving privacy.
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.