Numerical Computing with Python

Dynamic programming

Dynamic programming is a sequential way of solving complex problems by breaking them down into sub-problems and solving each of them. Once it solves the sub-problems, then it puts those subproblem solutions together to solve the original complex problem. In the reinforcement learning world, Dynamic Programming is a solution methodology to compute optimal policies given a perfect model of the environment as a Markov Decision Process (MDP).

Dynamic programming holds good for problems which have the following two properties. MDPs, in fact, satisfy both properties, which makes DP a good fit for solving them by solving Bellman Equations:

• Optimal substructure
  • Principle of optimality applies
  • Optimal solution can be decomposed ...