Vincent was playing a card game against his girlfriend and he kept loosing. So he wanted to train a bot to play on his behalf. This is our story.
We’re using AWS Lambda to get better at a card game named SushiGO. We make a small genetic algorithm in Python that uses AWS Lambda as a backend. The talk consists of these parts:
- Quick Explanation of the rules of the SushiGo Card Game
- Translation of real life to an algorithm
- Explain why this problem needs a lot of CPU
- Explain why AWS Lambda fits the simulation use-case
- How to quickly hack Concurrency in Python
- How to deploy lambda very quickly with chalice
- Experimentation Results
This talk will discuss an algorithm that we’ve tried to improve in three ways:
- Applying simple maths to make the search algorithm better
- Throwing lots (lots!) of CPU’s against the problem by leveraging AWS Lambda and python concurrency
We will conclude by discussing whether or not AWS Lambda is suitable for a gridsearch/grid simulation (hint, it’s not meant for this task, but it actually kind of works very well).