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This article discusses the use of reinforcement learning in developing complex learning systems. It explains how this approach allows the system to become better than its teacher and how heuristic algorithms and function approximation can be incorporated to cope with high-dimensional spaces. The article also introduces the concept of local models and presents convergence proofs for heuristic dynamic programming algorithms.
