Dynamic programming (DP) in reinforcement learning involves solving the reinforcement learning problem by breaking it down into smaller subproblems and solving them iteratively. DP methods, such as value iteration and policy iteration, require knowledge of the environment’s transition probabilities and rewards, which are often stored in a model of the environment.
The goal of DP in RL is to compute the optimal value function or policy using methods that involve recursive updates. In value iteration, for example, the value of each state is updated based on the values of the neighboring states, and the process is repeated until convergence. Similarly, policy iteration alternates between policy evaluation (calculating the value function) and policy improvement (updating the policy).
Dynamic programming requires a complete model of the environment, which limits its applicability in real-world problems where such models may not be available. It is most useful in small, fully known environments.