
Handbook of Learning and Approximate Dynamic Programming
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A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented The contributors are leading researchers in the field.
ADP or Approximate Dynamic Programming has gone by many different names including: Reinforcement Learning (RL), Adaptive Ccritics (AC), and Neuro-Dynamic Programming (NDP). The dynamic programming approach to decision and control problems involving nonlinear dynamic systems provides the optimal solution in any stochastic or uncertain environment. This handbook presents general-purpose programming tools for doing optimization over time by using learning and approximation to handle problems that severely challenge conventional methods.