A Theory of Abstraction in Reinforcement Learning
Author: David Abel
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Description: A Theory of Abstraction in Reinforcement Learning by David Abel presents a dissertation on abstraction in RL, outlining desiderata for effective abstraction functions and analyzing related algorithms. This work is valuable for understanding how agents can learn to make decisions in complex environments by forming concise models through abstraction.
Pages: 297
Megabytes: 4.39 MB
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