Trustworthy Reinforcement Learning for Multi-Agent Systems: Foundations of Robust and Accountable Decision Making

强化学习是一种在不确定性下建模和自动化顺序决策的计算方法。强化学习的最新进展凸显了在高风险领域（如推荐系统、交通或教育）利用这种方法的巨大潜力。然而，已经提出了许多问题，关于国家的最先进的强化学习技术的适用性，以高利益domestics.In这个项目中，我们提出了一个框架，设计值得信赖的和可靠的强化学习算法，因为他们没有考虑到在现实世界中的场景，包括其多智能体结构的复杂性。我们确定了设计这样一个框架，代理设计（设计强化学习算法本身）和系统设计（设计支持工具，提高代理的学习过程）的两个重要组成部分。在每个组件中，我们将研究这样一个框架应该具有的两个重要属性，以便被认为是值得信赖的：鲁棒性（处理对手和不确定性的能力）和问责制（为自己的行为提供解释的能力）。在鲁棒性的背景下，该建议阐述了代理的设计，是强大的其他代理，包括对手的存在，以及系统的设计，支持强大的学习，通过信道的可信信息。在问责方面，该提议详细说明了如何设计能够解释其行动的代理人，以及如何设计通过分配结果责任来支持问责的制度。该提案进一步概述了解决与这四个研究方向相关的技术挑战的议程，并提出了依赖于多智能体学习、强化学习和博弈论的方法。拟议的议程侧重于稳健和负责任的决策的理论和算法方面，为多智能体系统的可靠强化学习提供基础步骤。

Reinforcement learning is a computational approach to modeling and automating sequential decision making under uncertainty. Recent advancements in reinforcement learning have highlighted the incredible potential of utilizing this approach in high-stake domains, such as recommendation systems, transportation, or education. However, many concerns have been raised regarding the applicability of the state-of-the-art reinforcement learning techniques to high stake domains, as they fail to account for complexities present in real-world scenarios, including their multi-agent structure.In this project, we propose a framework for designing trustworthy and reliable reinforcement learning algorithms. We identify two important components in designing such a framework, agent design (designing reinforcement learning algorithms themselves) and system design (designing supporting tools that improve the learning processes of agents). Within each of these components, we will study two important properties that such a framework ought to have in order to be deemed trustworthy: robustness (ability to deal with adversaries and uncertainty) and accountability (ability to provide an account for one’s behavior). In the context of robustness, this proposal explicates the design of agents that are robust to the presence of other agents, including adversaries, as well as the design of systems that support robust learning through channeling trusted information. In the context of accountability, this proposal explicates the design of agents that can provide explanations for their actions, as well as the design of systems that support accountability by assigning responsibility for the outcomes. The proposal further outlines the agenda for resolving technical challenges related to these four research directions, and proposes approaches that rely on multi-agent learning, reinforcement learning, and game theory. The proposed agenda focuses on theoretical and algorithmic aspects of robust and accountable decision making that provide foundational steps toward trustworthy reinforcement learning for multi-agent systems.