论文标题
迈向多代理强化学习的政策解释
Toward Policy Explanations for Multi-Agent Reinforcement Learning
论文作者
论文摘要
多机构增强学习（MARL）的进步可以为一系列令人兴奋的多代理应用程序（例如合作AI和自主驾驶）提供顺序决策。解释代理决策对于提高系统透明度，提高用户满意度以及促进人类代理协作至关重要。但是，现有的有关可解释的强化学习的作品主要集中在单一代理设置上，并且不适合解决由多机构环境提出的挑战。我们提出了新的方法来生成MARL的两种类型的政策解释：（i）有关代理合作和任务顺序的政策摘要，以及（ii）语言解释以回答有关代理行为的查询。三个MARL结构域的实验结果证明了我们方法的可扩展性。一项用户研究表明，生成的解释可显着提高用户性能，并提高对用户满意度等指标的主观评分。
Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving. Explaining agent decisions is crucial for improving system transparency, increasing user satisfaction, and facilitating human-agent collaboration. However, existing works on explainable reinforcement learning mostly focus on the single-agent setting and are not suitable for addressing challenges posed by multi-agent environments. We present novel methods to generate two types of policy explanations for MARL: (i) policy summarization about the agent cooperation and task sequence, and (ii) language explanations to answer queries about agent behavior. Experimental results on three MARL domains demonstrate the scalability of our methods. A user study shows that the generated explanations significantly improve user performance and increase subjective ratings on metrics such as user satisfaction.
