Dynamic Programming
Dynamic Programming #
Dynamic Programming (DP) solves a reinforcement learning problem by repeatedly applying Bellman equations. It uses a complete model of the environment to calculate value functions and improve a policy.
Dynamic Programming turns a known model of an MDP into a policy for choosing actions.- Dynamic Programming
- Policy Iteration
- Value Iteration
- Generalized policy iteration
- Efficiency of Dynamic Programming
1. When Dynamic Programming Can Be Used ☆ #
DP assumes that the environment dynamics are known. For every state and action, the agent needs the probabilities of possible next states and rewards: