Prashant J. Viradiya
Amit M. Goswami
Hirenkumar K. Mistry
- Assistant Professor, Department of Computer Engineering, Research Scholar, Gyanmanjari Innovative University (GMIU), Bhavnagar, Gujarat, India
- Data Analyst, IT Department, Gyanmanjari Innovative University (GMIU), Bhavnagar, Gujarat, India
- Software Engineer, IT Department, Gyanmanjari Innovative University (GMIU), Bhavnagar, Gujarat, India
Abstract
This work provides an overview of Reinforcement Learning (RL), an important field of artificial intelligence (AI) aims to provide the long-term benefits by learning a relating with a given environment. It spells out everything, what agents and environments do, to how rewards, states, and behaviours. It spent lot of time on looking the most usable RL algorithms, like DQN, SARSA, and Q-Learning. These studies provide a clear view of RL. It can be used in real life in many areas, like healthcare, robots, games, and self-driving cars. It provides a new idea for AlphaGo and self-driving warehouse robots to do this. Along with this probable future uses and study gaps, new developments in RL are also shown. The last part on talks about the effects of RL and how it might be used in the future. The study’s main objective is to give a short summary of RL, by including its current state, problems, and possible future directions, with a focus on how it changed over time help to make a technology better.
Keywords: Reinforcement Learning, Q-Learning, Robotics, Artificial Intelligence, SARSA, Deep Q-Networks.
[This article belongs to E-Commerce for Future & Trends(ecft)]
References
- Brunke L, Greeff M, Hall AW, Yuan Z, Zhou S, Panerati J, Schoellig AP. Safe learning in robotics: From learning-based control to safe reinforcement learning. Annu Rev Control Robot Auton Syst. 2022 May 3; 5: 411–444.
- Canese L, Cardarilli GC, Di Nunzio L, Fazzolari R, Giardino D, Re M, Spanò S. Multi-agent reinforcement learning: A review of challenges and applications. Appl Sci. 2021 May 27; 11(11): 4948.
- Moerland TM, Broekens J, Plaat A, Jonker CM. Model-based reinforcement learning: A survey. Found Trends Mach Learn. 2023 Jan 3; 16(1): 1–18.
- Dulac-Arnold G, Mankowitz D, Hester T. Challenges of real-world reinforcement learning. arXiv preprint arXiv:1904.12901. 2019 Apr 29.
- Busoniu L, Babuska R, De Schutter B, Ernst D. Reinforcement learning and dynamic programming using function approximators. Florida, United States; 2017 Jul 28.
- Sana F, Azad NL, Raahemifar K. Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios: A Review. Machines. 2023 Jun 23; 11(7): 676.
- Muhalia E, Ngugi P, Moronge M. Effect of Transportation Management Systems on Supply Chain Performance of Fmcg In Kenya. Am J Supply Chain Manag. 2021 Jan 9; 6(1): 1–2.
- Oh J, Hessel M, Czarnecki WM, Xu Z, van Hasselt HP, Singh S, Silver D. Discovering reinforcement learning algorithms. Adv Neural Inf Process Syst. 2020; 33: 1060–70.
- West DM. The future of work: Robots, AI, and automation. Brookings Institution Press; 2018 May 15.
- Li SE. Deep reinforcement learning. In Reinforcement Learning for Sequential Decision and Optimal Control. Singapore: Springer Nature Singapore; 2023 Apr 6; 365–402.
E-Commerce for Future & Trends
| Volume | 11 |
| Issue | 01 |
| Received | January 17, 2024 |
| Accepted | February 13, 2024 |
| Published | March 28, 2024 |