![]() Representations, or basis functions, for MDPs are abstractions of the problem’s state space and are used to approximate value functions, which quantify the expected long-term utility obtained by following a policy. This dissertation builds on a recently introduced graph-based approach to learning representations for sequential decision-making problems modeled as Markov decision processes (MDPs). ![]() The ability to autonomously construct useful representations and to efficiently exploit them is an important challenge for artificial intelligence. This is not true for artificial systems, which have largely relied on humans to provide appropriate representations. Humans excel at finding appropriate representations for solving complex problems. Both tasks can be solved, but it is clearly more difficult to use the Roman numeral representations of twelve and twenty-four. ![]() For example, consider the task of multiplying the numbers 12 and 24. The ease or difficulty in solving a problem strongly depends on the way it is represented. Jeffrey T Johns, University of Massachusetts Amherst Basis construction and utilization for Markov decision processes using graphs
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