06-28, 14:30–14:50 (Asia/Jerusalem), PyData
Wordle is an online word game that has gone viral with millions of daily players world-wide. We will consider strategies based on information theory and reinforcement learning, allowing the creation of agents outperforming most human Wordle players.
Have you seen the posts on social media featuring yellow, green and gray boxes? Yes, that’s Wordle, a simple online word game that has gone viral with millions of daily players world-wide.
Though being a simple game, naive automatic solutions do not provide a winning strategy for the game. Following the success of machine learning solvers in games like chess and go, we will dive into Wordle and demonstrate how to program python agents that outperform most human players.
We will implement a strategy based on information theory and a strategy based on reinforcement learning. We will present a Wordle python package for evaluating our agents, which you can later use for evaluating and comparing your own agent.
Finally, we address the question all players are asking: What is the best starter word?
English
Target audience –Data Scientists
I’m the Lead Data Scientist at Intezer, a Genetic Malware Analysis startup, where I deliver end-to-end ML solutions for cyber threat detection. As part of my MSc, I explore NLP and time series analysis techniques for the detection of radicalization in social networks. I hold a BSc in Statistics and Computer Science and have served in 8200.