Real-Time Recurrent Learning using Trace Units in Reinforcement Learning

TL;DR

This paper introduces Recurrent Trace Units (RTUs), a lightweight modification of linear recurrent architectures, that significantly improve online reinforcement learning performance with efficient real-time recurrent learning.

Contribution

The paper proposes RTUs, a novel recurrent architecture that enhances RTRL efficiency and effectiveness in online reinforcement learning scenarios.

Findings

RTUs outperform other recurrent architectures in partially observable environments.

RTUs require less computation while achieving better performance.

RTUs demonstrate significant improvements over LRUs.

Abstract

Recurrent Neural Networks (RNNs) are used to learn representations in partially observable environments. For agents that learn online and continually interact with the environment, it is desirable to train RNNs with real-time recurrent learning (RTRL); unfortunately, RTRL is prohibitively expensive for standard RNNs. A promising direction is to use linear recurrent architectures (LRUs), where dense recurrent weights are replaced with a complex-valued diagonal, making RTRL efficient. In this work, we build on these insights to provide a lightweight but effective approach for training RNNs in online RL. We introduce Recurrent Trace Units (RTUs), a small modification on LRUs that we nonetheless find to have significant performance benefits over LRUs when trained with RTRL. We find RTUs significantly outperform other recurrent architectures across several partially observable environments while using significantly less computation.