Y et this approach does not scale to high dimensions.

Transplant-organs are a scarce medical resource.

Human explanation (e.g., in terms of feature importance) has been recently used

To learn good joint policies for multi-agent collaboration with imperfect information remains a fundamental challenge.

We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components.

The machines gain possibly different utilities by processing different jobs, and all jobs assigned to the same machine should be processed without overlap.

Multi-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems. In many applications, effective understanding of the situation and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision making and planning.