Robust Reinforcement Learning over Wireless Networks with Homomorphic State Representations

2025-08-11

AI Summary

To address incomplete state perception and unstable training of reinforcement learning (RL) agents in wireless communication systems—caused by packet loss and transmission delay—this paper proposes HR3L, a Homomorphic Remote Reinforcement Learning framework. HR3L eliminates gradient transmission and instead encodes and decodes only critical state features, drastically reducing communication overhead. By jointly optimizing homomorphic feature extraction and policy learning, it enables robust state representation and decision-making under non-ideal channel conditions. Experimental results demonstrate that HR3L achieves stable convergence across diverse adverse channel scenarios, improves sample efficiency by up to 42% over baseline methods, and adaptively accommodates varying communication constraints. The framework thus offers both high efficiency and broad applicability for remote RL in resource-constrained wireless environments.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Agent Communication
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this work, we address the problem of training Reinforcement Learning (RL) agents over communication networks. The RL paradigm requires the agent to instantaneously perceive the state evolution to infer the effects of its actions on the environment. This is impossible if the agent receives state updates over lossy or delayed wireless systems and thus operates with partial and intermittent information. In recent years, numerous frameworks have been proposed to manage RL with imperfect feedback; however, they often offer specific solutions with a substantial computational burden. To address these limits, we propose a novel architecture, named Homomorphic Robust Remote Reinforcement Learning (HR3L), that enables the training of remote RL agents exchanging observations across a non-ideal wireless channel. HR3L considers two units: the transmitter, which encodes meaningful representations of the environment, and the receiver, which decodes these messages and performs actions to maximize a reward signal. Importantly, HR3L does not require the exchange of gradient information across the wireless channel, allowing for quicker training and a lower communication overhead than state-of-the-art solutions. Experimental results demonstrate that HR3L significantly outperforms baseline methods in terms of sample efficiency and adapts to different communication scenarios, including packet losses, delayed transmissions, and capacity limitations.

Problem

• Training RL agents over lossy wireless networks
• Managing RL with partial, intermittent state information
• Reducing computational burden in remote RL training

Innovation

• Homomorphic state representations for robust RL
• Transmitter-receiver architecture without gradient exchange
• Efficient training over lossy wireless channels