Strong and Compact Policies for Submodular Markov Decision Processes via LP-Based Submodular Orienteering

Finding policies for Markov Decision Processes (MDPs) is a central problem in areas such as Reinforcement Learning and Operations Research. Here, we have to repeatedly choose an action that should be performed by an agent. Depending on the action and the current state of the agent, the agent collect