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Enhancing stability and explainability in reinforcement learning with machine learning
- 1 Central South University
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Abstract
In the field of reinforcement learning, training agents using machine learning algorithms to learn and perform tasks in complex environments has become a prevalent approach. However, reinforcement learning faces challenges such as training instability and decision opacity, which limit its feasibility in real-world applications. To solve the problems of stability and transparency in reinforcement learning, this project will use advanced algorithms like Proximal Policy Optimization (PPO), Q-DAGGER, and Gradient Boosting Decision Trees to set up reinforcement learning agents in the OpenAI Gymnasium environment. Specifically, the study selected the Atari game Breakout as the testbed, enhancing training efficiency and game performance by refining reward structures and decision-making processes, and integrating interpretable models to provide explanations for agent decisions. This study has successfully developed robust reinforcement learning agents that excel in complex environments. By employing advanced algorithms like PPO, Q-DAGGER, and Gradient Boosting Decision Trees, the study has addressed issues of training instability, and improved game performance through optimized reward structures and decision processes. Additionally, by integrating interpretable models, the study has provided insights into the learned strategies of the agents, thereby enhancing decision transparency. These findings provide crucial support for the broader application of reinforcement learning in real-world scenarios and offer valuable insights for tackling other complex tasks.
Keywords
Reinforcement learning, Explainable artificial intelligence, Proximal policy gradient, GBDT.
[1]. Reinforcement Learning Stability: Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
[2]. Machine Learning for Decision Making: Ribeiro, M.T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
[3]. Proximal Policy Optimization (PPO): Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
[4]. Q-DAGGER: Ross, S., & Bagnell, D. (2010). Efficient reductions for imitation learning. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 661-668).
[5]. OpenAI Gymnasium: Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016). OpenAI gym. arXiv preprint arXiv:1606.01540.
[6]. Reinforcement Learning Stability: Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
[7]. Interpretable Machine Learning Models: Molnar, C. (2020). Interpretable machine learning. Lulu.com.
[8]. Machine Learning for Decision Making: Ribeiro, M.T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
[9]. Computational Demands in AI Research: Jordan, M.I., & Mitchell, T.M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
[10]. Visual Methods for Model Interpretability: Zeiler, M.D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818-833).
Cite this article
Chen,Y. (2024). Enhancing stability and explainability in reinforcement learning with machine learning. Applied and Computational Engineering,101,25-34.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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