Investigating the Practicality of Existing Reinforcement Learning Algorithms: A Performance Comparison

Abstract

Reinforcement learning (RL) has become more popular due to promising results in applications such as chat-bots, healthcare, and autonomous driving. However, one significant challenge in current RL research is the difficulty in understanding which RL algorithms, if any, are practical for a given use case. Few RL algorithms are rigorously tested, and hence understood, for their practical implications. Although there are a number of performance comparisons in literature, many use few environments and do not consider real-world limitations such as run-time and memory usage. Furthermore, many works do not make their code publicly accessible for others to use. This paper addresses this gap by presenting the most comprehensive performance comparison on the practicality of RL algorithms known to date. Specifically, this paper focuses on discrete, model-free deep RL algorithms for their practicality in real-world problems where efficient implementations are necessary. In total, fourteen RL algorithms were trained on twenty-three environments (468 environment instances), which collectively required 224 GB and 766 days CPU time to run all experiments, and 1.7 GB to store all models. Overall, the results indicate several shortcomings in RL algorithms’ exploration efficiency, memory/sample efficiency, and space/time complexity. Based on these shortcomings, numerous opportunities for future works were identified to improve the capabilities of modern algorithms. This paper’s findings will help researchers and practitioners improve and employ RL algorithms in time-sensitive and resource-constrained applications such as economics, cybersecurity, and Internet of Things (IoT).

Publication
Institute of Electrical and Electronics Engineers TechRxiv
Olivia Dizon-Paradis
Olivia Dizon-Paradis
Graduate Research Assistant

My research interests include artificial intelligence, machine learning, computer vision, and reinforcement learning