arXiv:2512.05291v3 Announce Type: replace Abstract: Actor-critic (AC) methods are a cornerstone of reinforcement learning (RL) but offer limited interpretability. Current explainable RL methods seldom use state attributions to assist training. Rather, they treat all state features equally, thereby neglecting the heterogeneous impacts of individual state dimensions on the reward. We propose RKHS-SHAP-based Advanced Actor-Critic (RSA2C), an attribution-aware, kernelized, two-timescale AC algorithm, including Actor, Value Critic, and Advantage Critic. The Actor is instantiated in a vector-valued
The increasing complexity and opacity of AI models, particularly in reinforcement learning, are driving a strong need for explainability to ensure reliability and trust.
This research advances explainable reinforcement learning, which is crucial for deploying autonomous AI systems in critical applications where understanding agent decisions is paramount.
The proposed RKHS-SHAP-based Advanced Actor-Critic (RSA2C) algorithm offers a method for building more interpretable reinforcement learning agents by incorporating state attributions into the training process.
- · AI developers
- · Robotics
- · Autonomous systems
- · Regulators
- · Black-box AI systems
- · Traditional reinforcement learning approaches without interpretability
More transparent and debuggable AI agents will be developed, accelerating their adoption in sensitive domains.
Reduced deployment risk for AI in areas like manufacturing, healthcare, and defense due to enhanced auditability and trust.
Increased public and regulatory acceptance of advanced AI, potentially leading to faster innovation cycles and broader societal integration of autonomous systems.
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