Navigating in Gridworld using Policy and Value Iteration
Learn how to use policy evaluation, policy iteration, and value iteration to find the shortest path in gridworld.
In reinforcement learning, we are interested in identifying a policy that maximizes the obtained reward. Assuming a perfect model of the environment as a Markov decision process (MDPs), we can apply dynamic programming methods to solve reinforcement learning problems.
In this post, I present three dynamic programming algorithms that can be used in the context of MDPs. To make these concepts more understandable, I implemented the algorithms in the context of a gridworld, which is a popular example for demonstrating reinforcement learning.
