Reinforcement Learning and Dynamic Programming Using Function Approximators

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From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart ...

Reinforcement Learning and Dynamic Programming Using Function Approximators 2010, CRC Press, Boca Raton, FL

ISBN-13: 9781439821084

Hardcover

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