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This chapter considers dynamic programming in continuous time when the state space is finite. The chapter begins with a treatment of linear dynamics in vector space and then identifies lifetime values with integrals over discounted expected rewards. The optimality theory uses a discrete version of the Hamilton-Jacobi-Bellman equation. The material serves as a jump-off point for learning continuous time optimization.
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