Understanding the agent's learning process, particularly the factors that contribute to its success or failure post-training, is crucial for comprehending the rationale behind the agent's decision-making process. Prior methods clarify the learning process by creating a structural causal model (SCM) or visually representing the distribution of value functions. Nevertheless, these approaches have constraints as they exclusively function in 2D-environments or with uncomplicated transition dynamics. Understanding the agent's learning process in complicated environments or tasks is more challenging. In this paper, we propose REVEAL-IT, a novel framework for explaining the learning process of an agent in complex environments. Initially, we visualize the policy structure and the agent's learning process for various training tasks. By visualizing these findings, we can understand how much a particular training task or stage affects the agent's performance in test. Then, a GNN-based explainer learns to highlight the most important section of the policy, providing a more clear and robust explanation of the agent's learning process. The experiments demonstrate that explanations derived from this framework can effectively help in the optimization of the training tasks, resulting in improved learning efficiency and final performance.

翻译：理解智能体的学习过程，特别是影响其训练后成败的关键因素，对于解析其决策逻辑至关重要。现有方法通常通过构建结构因果模型或可视化价值函数分布来阐明学习过程。然而，这些方法存在局限性：仅适用于二维环境或具有简单转移动态的场景。在复杂环境或任务中理解智能体的学习过程更具挑战性。本文提出REVEAL-IT这一创新框架，用于解释智能体在复杂环境中的学习过程。首先，我们针对不同训练任务可视化策略结构及智能体的学习轨迹。通过可视化呈现，我们可以量化特定训练任务或阶段对智能体测试表现的影响程度。随后，基于图神经网络的解释器学习识别策略中最重要的部分，从而为智能体学习过程提供更清晰、更鲁棒的解释。实验表明，该框架生成的解释能有效指导训练任务优化，显著提升学习效率与最终性能。