Enhancing Sample Efficiency and Privacy in Real-World Decision-Making: A Federated Reinforcement Learning Approach
Abstract:
This thesis explores the enhancement of sample efficiency and privacy in real-world decision-making through Federated Reinforcement Learning (FedRL). As Reinforcement Learning (RL) applications transition from simulations to real-world scenarios, they encounter significant challenges, such as poor sample efficiency and stringent privacy requirements. FedRL addresses these by enabling a collaborative learning process among distributed agents, who learn from their local environments without sharing sensitive data. The contributions of this work are structured into several frameworks, each addressing a core aspect of FedRL:
Pioneering theoretical foundation: We pioneer the theoretical foundation of FedRL by introducing FedPG-BR, a pioneering federated policy gradient framework. By utilizing variance-reduced policy gradients and robust gradient aggregation method, FedPG-BR ensures that each agent achieves better outcomes with fewer interactions, thereby enhancing system resilience and sample efficiency.
Provably Fast Convergence and Decentralization: The DecByzPG framework is introduced to integrate state-of-the-art convergence rates of policy gradient methods with Byzantine fault tolerance in a decentralized architecture. By eliminating reliance on a central server, DecByzPG reduces vulnerabilities and enhances the robustness of the learning network.
Handling Heterogeneity: Two frameworks, FedHQL and CAESAR, are developed to handle agent and environmental heterogeneity, respectively. FedHQL manages varied agent capabilities, while CAESAR handles differences in environmental dynamics. Both frameworks ensure that despite these variations, the FedRL system remains effective and consistent across diverse settings.
Accounting Human Aspects: Building upon these foundational frameworks, we introduce FedRLHF (Federated Reinforcement Learning with Human Feedback). FedRLHF tackles the critical challenges of privacy preservation and personalization inherent in traditional RLHF methods, which rely on centralized data aggregation. By decentralizing the RLHF process, FedRLHF enables agents to incorporate human feedback locally without compromising user privacy. Theoretical analyses provide convergence guarantees and derive sample complexity bounds that scale efficiently with the number of clients. Empirical evaluations on the MovieLens and IMDb datasets demonstrate that FedRLHF preserves user privacy, matches the performance of centralized RLHF, and enhances personalization across diverse client environments.
Through these frameworks, this thesis significantly advances real-world decision-making via Reinforcement Learning by enhancing sample efficiency and ensuring robust privacy preservation. The introduction of FedRLHF exemplifies the practical application of FedRL principles in real-world scenarios, effectively integrating human feedback to achieve personalized and secure decision-making processes. Collectively, these contributions pave the way for deploying efficient, privacy-conscious, and personalized RL systems across various domains.