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Algorithms for reinforcement learning

By: Material type: TextSeries: Synthesis lectures on artificial intelligence and machine learning #9Publication details: Morgan & Claypool, 2010 UK:Description: xii, 89 p. : ill.; 23.5 cmISBN:
  • 9781608454921
Subject(s): DDC classification:
  • 006.31 SZE
Summary: Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'
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Books DAU 006.31 SZE Available 031611

Includes bibliographical references.

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms'

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