Towards Reinforcement Learning in the Continuing Setting

Many sequential decision making problems can be naturally formulated as continuing tasks in which the agent-environment interaction goes on forever without limit. Unlike the episodic case, reinforcement learning (RL) solution methods for the continuing setting are not well understood, theoretically or empirically. RL research lacks a collection of easy-to-use continuing problems that can help foster our understanding of the problem setting and its solution methods. To stimulate research in the RL methods for the continuing setting, we sketch a preliminary set of continuing problems that we refer to as C-suite. We invite the workshop attendees to further refine the sketch and contribute new problems that isolate specific research issues that arise in the continuing setting.