Recent advances in multi-agent reinforcement learning (MARL) provide a variety of tools that support the ability of agents to adapt to unexpected changes in their environment, and to operate successfully given their environment's dynamic nature (which may be intensified by the presence of other agents). In this work, we highlight the relationship between a group's ability to collaborate effectively and the group's resilience, which we measure as the group's ability to adapt to perturbations in the environment. To promote resilience, we suggest facilitating collaboration via a novel confusion-based communication protocol according to which agents broadcast observations that are misaligned with their previous experiences. We allow decisions regarding the width and frequency of messages to be learned autonomously by agents, which are incentivized to reduce confusion. We present empirical evaluation of our approach in a variety of MARL settings.
翻译:多剂强化学习(MARL)的最近进展提供了各种工具,支持代理人适应其环境意外变化的能力,并鉴于其环境的动态性质(其他代理人的存在可能会加强这种能力)成功运作的能力。在这项工作中,我们强调一个集团有效协作的能力与集团的复原力之间的关系,我们衡量这是该集团适应环境扰动的能力。为了提高复原力,我们建议通过新的基于混乱的通信协议促进协作,根据该协议,代理人传播与其以往经验不相符的观测结果。我们允许代理人自主地了解关于信息的宽度和频度的决定,这些决定激励人们减少混乱。我们从经验角度评价我们在各种MAL环境中的做法。