Reproduce “Asynchronous Policy Gradient Aggregation for Efficient Distributed Reinforcement Learning” on OpenResearch

About this paper

Alexander Tyurin, Andrei Spiridonov, Varvara Rudenko

Researchers from AXXX and the Applied AI Institute introduced Rennala NIGT and Malenia NIGT, new asynchronous policy gradient aggregation algorithms designed for efficient distributed reinforcement learning. These algorithms improve upon existing methods by achieving better computational and communication time complexities, such as an O(κε⁻²) communication complexity which improves on AFedPG's O(κε⁻³), and robustly handle heterogeneous environments and computational resources.

Key insights

• Asynchronous aggregation schemes that prioritize contributions from faster agents can significantly improve computational efficiency and robustness in heterogeneous distributed RL settings.
• Integrating momentum and normalization techniques into asynchronous distributed policy gradient methods can yield superior theoretical guarantees for convergence rates and time complexities.
• Developing distinct but related algorithms, like Rennala NIGT and Malenia NIGT, allows for optimized performance and broader applicability across both homogeneous and heterogeneous distributed learning environments, including federated RL.

Summary by alphaXiv.