AI

This blog post concerns a famous toy problem in Reinforcement Learning, the [FrozenLake environment](https://gym.openai.com/envs/FrozenLake-v0/). We compare solving an environment with RL by reaching **maximum performance** versus obtaining the **true state-action values** $Q_{s,a}$.

In this blogpost, we solve a famous sequential decision problem called Jacks Car Rental by first turning it into a Gym environment and then use a RL algorithm called Policy Iteration (a form of Dynamic Programming) to solve for the optimal decisions to take in this environment.