Pwnagotchi: Deep Reinforcement Learning for WiFi pwning!

Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs.

Learn more about the project and how it started on the author’s blog.

Instead of merely playing Super Mario or Atari games like most reinforcement learning based “AI” (yawn), Pwnagotchi tunes its own parameters over time to get better at pwning WiFi things in the real world environments you expose it to.

Learn more about how Pwnagotchi works and why it eats WPA handshakes in the Introduction doc. You can also read about the story of the project.


To give hackers an excuse to learn about reinforcement learning and WiFi networking—and have a reason to get out for more walks.

Also? It’s cute as f—.

In case you're curious about the name: Pwnagotchi (ポーナゴッチ) is a portmanteau of pwn and -gotchi. It is a nostalgic reference made in homage to a very popular children's toy from the 1990s called the Tamagotchi. The Tamagotchi (たまごっち, derived from tamago (たまご) "egg" + uotchi (ウオッチ) "watch") is a cultural touchstone for many Millennial hackers as a formative electronic toy from our collective childhoods.

Were you lucky enough to possess a Tamagotchi as a kid? Well, with your Pwnagotchi, you too can enjoy the nostalgic delight of being strangely emotionally attached to a handheld automata yet again! Except, this time get to #HackThePlanet. >:D