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A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem
To help bridge the gap between novel and existing methods, we propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) that can leverage an existing dispatch method to minimize system cost.
Approximate Kalman Filter Q-Learning for Continuous State-Space MDPs
We seek to learn an effective policy for a Markov Decision Process (MDP) with continuous states via Q-Learning.
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