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Another key feature of reinforcement learning is that it explicitly considers the whole problem of a goal-directed agent interacting with an uncertain environment. info WHAT IS REINFORCEMENT LEARNING? 15 This is in contrast with many approaches that consider subproblems without addressing how they might ﬁt into a larger picture. For example, we have mentioned that much of machine-learning research is concerned with supervised learning without explicitly specifying how such ability would ﬁnally be useful.
The representative DSD is used for decision making and allows multiperspective decision making. In the case of no knowledge of dependency from a particular perspective, the representative DSD will not represent that particular perspective. More and more information with reference to perspectives is incorporated in representative DSD. 6 Qualitative Belief Network and ID Bayesian belief network or inﬂuence diagrams acknowledge the usefulness of these frameworks for addressing complex, dynamic real-life problems.
With broader and holistic system knowledge, it can deal with complex decision problems in a more organized way to provide best decisions. REFERENCE 1. Senge P. The Fifth Discipline—The Art & Practice of The Learning Organization. Currency Doubleday, New York, 1990. 1 INTRODUCTION As we have discussed in Chapter 1, learning refers to mathematical representation and extrapolation of data and experiences based on input and output mapping. It is generally data centric, and these data are either pattern or event based.