Suppose that preferences in a stable matching instance are universal: all n jobs share the prefer- ences C₁ C₂ > ... > C₂ and all candidates share the preferences J₁ > J₂ > · · · > Jn. (a) What pairing do we get from running the algorithm with jobs proposing? Can you prove this happens for all n? (b) What pairing do we get from running the algorithm with candidates proposing? (c) What does this tell us about the number of stable pairings?

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Temporal difference learning

Temporal difference learning (TD learning) is a concept in machine learning that attributes to a class of model-free reinforcement learning methods. The technique predicts a reward or quantity depending on the future values of the value function.