Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding

TL;DR

This paper introduces HR3L, a novel RL architecture that enables robust training over unreliable channels without gradient exchange, improving efficiency and adaptability in lossy communication environments.

Contribution

The paper presents HR3L, a new homomorphic encoding-based architecture for remote RL that reduces communication overhead and handles unreliable channels effectively.

Findings

HR3L outperforms existing methods in sample efficiency.

HR3L adapts well to packet loss, delays, and bandwidth limits.

Training speed and communication costs are significantly improved.

Abstract

Traditional Reinforcement Learning (RL) frameworks generally assume that the agent perceives the state of the underlying Markov process instantaneously and then takes actions accordingly. If the agent cannot directly observe the process, but rather receives state updates from a remote sensor over a lossy and/or delayed channel, it may be forced to operate with partial and intermittent information. In recent years, numerous learning architectures have been proposed to manage RL with imperfect or remote feedback; however, they offer solutions tailored to specific use cases, often with a substantial computational and communication burden. To address these limitations, we propose a novel learning architecture, named Homomorphic Robust Remote Reinforcement Learning (HR3L), that enables the distributed training of RL agents over unreliable communication channels without the need to exchange gradient information. Our experimental results demonstrate that HR3L significantly outperforms the state-of-the-art methods in terms of sample efficiency, leading to faster training and reduced communication overhead. In addition, we show that HR3L can adapt to different scenarios, including packet loss, delayed transmissions, and bandwidth limitations, without experiencing significant performance degradation.