A Review of Reinforcement Learning: Current Trends and Future Prospects in Autonomous Systems
Chreesk Sabah M. Ali, Hajar Maseeh Yasin
Asian Journal of Research in Computer Science · pp. 85–104 · Published 10 Feb 2025
10.9734/ajrcos/2025/v18i3581Abstract
This review focuses on the use of reinforcement learning (RL) for autonomous systems and current trends and future prospects. It is therefore the intended goal to critically evaluate the concept of RL for improving autonomous decision making with focus on current and emerging issues including; sample efficiency, scalability, and safety. This review methodology is a synthesis of 10 studies which has been conducted between the years 2021 and 2024. However, these are some of the challenges that seem to plague RL even as it has potential to be used in realistic applications such as robots, self-driving cars and smart grid. The review also opines that due to developments of algorithms, computer intrinsics and safety mechanism, RL perhaps holds the key to the future for autonomous systems.
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