Design and Implementation of Deep Reinforcement Learning Models for Autonomous Decision-Making in Dynamic Environments
Keywords:
deep reinforcement learning, autonomous decision-making, dynamic environments, policy optimisation, generalisation, artificial intelligenceAbstract
This study examines the design and implementation of deep reinforcement learning models for autonomous decision-making in dynamic environments characterised by uncertainty, high-dimensional state spaces, and continuous change. The research adopts a secondary data methodology, synthesising recent scholarly literature to analyse key algorithms, including value-based, policy-gradient, and actor–critic approaches. The findings indicate that advanced methods such as Proximal Policy Optimisation and Soft Actor-Critic demonstrate improved stability, exploration efficiency, and adaptability compared to earlier techniques. The study also identifies critical challenges, including sample inefficiency, limited generalisation, and safety concerns, which constrain real-world deployment. Furthermore, the analysis highlights the importance of representation learning, distributed training, and domain adaptation in enhancing system performance. The research contributes to both theoretical understanding and practical insights by evaluating how deep reinforcement learning frameworks can be optimised for reliable autonomous decision-making in complex and evolving environments.
References
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine, 34(6), 26–38.
Cobbe, K., Klimov, O., Hesse, C., Kim, T., & Schulman, J. (2019). Quantifying generalisation in reinforcement learning. Proceedings of the 36th International Conference on Machine Learning, 1282–1289.
Dulac-Arnold, G., Mankowitz, D., & Hester, T. (2019). Challenges of real-world reinforcement learning. arXiv preprint arXiv:1904.12901.
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., & Kavukcuoglu, K. (2018). IMPALA: Scalable distributed deep-RL with importance weighted actor-learner architectures. Proceedings of the 35th International Conference on Machine Learning, 1407–1416.
Foerster, J. N., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. (2018). Counterfactual multi-agent policy gradients. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 2974–2982.
García, J., & Fernández, F. (2015). A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research, 16, 1437–1480.
Haarnoja, T., Zhou, A., Abbeel, P., & Levine, S. (2018). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. Proceedings of the 35th International Conference on Machine Learning, 1861–1870.
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018). Deep reinforcement learning that matters. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 3207–3214.
Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2016). End-to-end training of deep visuomotor policies. Journal of Machine Learning Research, 17(39), 1–40.
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2016). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533.
OpenAI, Andrychowicz, M., Baker, B., Chociej, M., Józefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., Schneider, J., Sidor, S., Tobin, J., Welinder, P., Weng, L., & Zaremba, W. (2019). Learning dexterous in-hand manipulation. The International Journal of Robotics Research, 39(1), 3–20.
Rashid, T., Samvelyan, M., De Witt, C. S., Farquhar, G., Foerster, J. N., & Whiteson, S. (2018). QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning. Proceedings of the 35th International Conference on Machine Learning, 4295–4304.
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimisation algorithms. arXiv preprint arXiv:1707.06347.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017). Domain randomisation for transferring deep neural networks from simulation to the real world. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 23–30.
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Oh, J., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J., Jaderberg, M., & Silver, D. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Engineering Science & Humanities
This work is licensed under a Creative Commons Attribution 4.0 International License.
