Explainable Artificial Intelligence (XAI) Approach for Reinforcement Learning Systems
Maria J. P. Peixoto, Akramul Azim · 2024
This study highlights the significance of incorporating uncertainty in Explainable Artificial Intelligence (XAI) systems. To achieve our purpose, we utilize Bayesian deep learning and uncertainty-aware planning, which allow us to create visual indicators to demonstrate how autonomous agents perceive their surroundings and take action. We use a reinforcement learning method called DQN with uncertainty and compare the results with four other baseline algorithms: DQN, DDQN with Prioritised Replay, Dueling DDQN and DDQN. Our findings show that the uncertainty estimate can generate a more efficient and stable decision-making model. To explain the behaviour of our agent, we suggest an interface that displays the current view of the environment, the agent's view, Q-values for each possible action, state value, a heatmap relative to the agent's input, and a visual representation of the uncertainty connected with each Q-value. Our approach can enhance the human understanding of the algorithm's decision-making process and confidence in its performance.