You need to be happy about Markov Decision Processes (the previous
Andrew Tutorial) before venturing into Reinforcement Learning. It concerns
the fascinating question of whether you can train a controller to
perform optimally in a world where it may be necessary to suck up
some short term punishment in order to achieve long term reward. We
will discuss certainty-equivalent RL, the Temporal Difference (TD)
learning, and finally Q-learning. The curse of dimensionality will
be constantly learning over our shoulder, salivating and cackling.
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