Reinforcement Learning

Navigating in Gridworld using Policy and Value Iteration

Navigating in Gridworld using Policy and Value Iteration

Learn how reinforcement learning algorithms such as policy evaluation, policy iteration, and value iteration can be used to find the shortest path in gridworld.

0

With perfect knowledge of the environment, reinforcement learning can be used to plan the behavior of an agent. In this post, I use gridworld to demonstrate three dynamic programming algorithms for Markov decision processes: policy evaluation, policy iteration, and value iteration.