Many real-world tasks involve multiple agents with partial observability and
limited communication. Learning is challenging in these settings due to local
viewpoints of agents, which perceive the world as non-stationary due to
concurrently-exploring teammates. Approaches that learn specialized policies
for individual tasks face problems when applied to the real world: not only do
agents have to learn and store distinct policies for each task, but in practice
identities of tasks are often non-observable, making these approaches
inapplicable. This paper formalizes and addresses the problem of multi-task
multi-agent reinforcement learning under partial observability. We introduce a
decentralized single-task learning approach that is robust to concurrent
interactions of teammates, and present an approach for distilling single-task
policies into a unified policy that performs well across multiple related
tasks, without explicit provision of task identity.

研究了多智能体下的多任务增强学习问题，提出了一种去中心化的单任务学习方法，并介绍了一种将单任务策略转化为适用于多个相关任务的统一策略的方法，无需明确标识任务身份。