Research on Reinforcement Learning Explainable Strategies Based on Advantage Saliency

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Deep reinforcement learning is increasingly being used in difficult environments with sparse rewards and high-dimensional inputs, and it performs well, but its decision-making processes are largely unclear and difficult to explain to end users. Saliency map methods explain an agent's behavior by highlighting state features relevant for the agent to take an action. In this paper, we use the perturbation-based saliency map method, propose the use of advantage function to replace the existing method of calculating state saliency, realize the combination of advantage function and perturbation-based saliency map. A saliency map is generated by noting the saliency of the dependent elements of the agent's chosen action in the Atari game environment. Experimental comparisons show that our method generates more accurate explanatory saliency maps.