This blogpost is about **Blood Bowl**, a boardgame I started playing last year. The idea of this blog post is to showcase some possible analyses that can be done on the FUMBBL match data I've compiled.
This blogpost is about **Blood Bowl**, a boardgame I started playing last year. The goal of this blog post is to use Python API and HTML scraping to fetch Blood Bowl match outcome data from FUMBBL.com, and to create a structured dataset ready for analysis and visualization.
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.