Exploration-Driven Personalized Federated Reinforcement Learning via Intrinsic Motivation
A recent paper on arXiv (2608.10499) has unveiled a novel framework for personalized federated reinforcement learning (PFRL), called Exploration-Driven Personalized Federated Reinforcement Learning via Intrinsic Motivation (EDPFRL-IM). This framework tackles the issue of exploration in environments characterized by sparse rewards or non-stationarity. Unlike traditional PFRL approaches that depend heavily on available reward signals, EDPFRL-IM emphasizes curiosity-driven exploration at each client, enhancing local exploration while safeguarding client privacy. Clients incorporate an intrinsic random network distillation (RND) signal alongside their extrinsic rewards to aid in discovering policies within uncharted state spaces. This significant work, authored by a group of researchers, represents a notable advancement in decentralized learning methods that prioritize data privacy while enhancing policy learning in intricate settings.
Key facts
- Paper arXiv:2608.10499 introduces EDPFRL-IM framework.
- EDPFRL-IM stands for Exploration-Driven Personalized Federated Reinforcement Learning via Intrinsic Motivation.
- The framework addresses exploration in non-stationary or sparse-reward environments.
- It uses curiosity-driven exploration at each client.
- Clients add an intrinsic random network distillation (RND) signal to their extrinsic reward.
- The approach promotes local exploration and protects client privacy.
- The paper is a cross-type announcement on arXiv.
- The work is relevant to personalized federated reinforcement learning.
Entities
Institutions
- arXiv