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Building upon the foundational work of “Explain to Gain: Introspective Reinforcement Learning for Enhanced Performance,” this article further investigates methods for leveraging explainable reinforcement learning (XRL) knowledge to enhance the performance of reinforcement learning (RL) agents. While our initial work demonstrated the potential of XRL approaches to guide and optimise RL agent training beyond merely improving interpretability and user trust, this paper extends that exploration. We expand upon the previously introduced introspective analysis framework by incorporating an additional XRL metric parameter into the search space of XRL parameter configurations within the training pipelines of model-free RL algorithms. This refined integration allows for even more nuanced dynamic adjustments of algorithm-specific parameters based on real-time feedback from a broader set of XRL metrics. The proposed methodology is validated across diverse OpenAI Gym environments (CartPole and Taxi). By evaluating both on-policy and off-policy approaches with this expanded parameter space, we demonstrate that incorporating these additional XRL insights leads to further significant improvements in agent performance. The analysis of the results highlights the enhanced benefits of deepened explainability and more finely tuned decision-making. This work contributes to the XRL research area by continuing to align interpretability with actionable performance gains, advancing the development of more reliable, transparent, and effective RL systems for complex, real-world applications.

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Explain to Gain: Optimising Performance Through Explainable Reinforcement Learning Parameter Investigation

  • 会议论文

  • 发表日期：

    2026年

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  • Patrick Capaldo,
  • Varniethan Ketheeswaran,
  • Santiago Quintana-Amate,
  • Delaney Stevens,
  • Mark Hall

摘要

Building upon the foundational work of “Explain to Gain: Introspective Reinforcement Learning for Enhanced Performance,” this article further investigates methods for leveraging explainable reinforcement learning (XRL) knowledge to enhance the performance of reinforcement learning (RL) agents. While our initial work demonstrated the potential of XRL approaches to guide and optimise RL agent training beyond merely improving interpretability and user trust, this paper extends that exploration. We expand upon the previously introduced introspective analysis framework by incorporating an additional XRL metric parameter into the search space of XRL parameter configurations within the training pipelines of model-free RL algorithms. This refined integration allows for even more nuanced dynamic adjustments of algorithm-specific parameters based on real-time feedback from a broader set of XRL metrics. The proposed methodology is validated across diverse OpenAI Gym environments (CartPole and Taxi). By evaluating both on-policy and off-policy approaches with this expanded parameter space, we demonstrate that incorporating these additional XRL insights leads to further significant improvements in agent performance. The analysis of the results highlights the enhanced benefits of deepened explainability and more finely tuned decision-making. This work contributes to the XRL research area by continuing to align interpretability with actionable performance gains, advancing the development of more reliable, transparent, and effective RL systems for complex, real-world applications.