It is a self-contained, teachable example of deep RL with self-play, running quickly on laptop hardware, and includes a browser demo showing the AI's thought process.
An AI that teaches itself to play (and bluff at) Liar's Dice, using Deep Reinforcement Learning in PyTorch. It starts knowing nothing and learns purely by playing 60,000 games against itself — in about 30 seconds on a normal laptop CPU (no GPU needed).
Built as a teaching example: small, self-contained, and fully runnable.
A bluffing game of hidden information (a cousin of Poker).
Because you can't see the opponent's dice, every move is a decision under uncertainty — the perfect playground for reinforcement learning.
This project is Deep RL = DL + RL: a neural network is the brain, trained by the reward of winning games.
| RL concept | In Liar's Dice |
|---|---|
| Agent | the AI player (a small neural network) |
| Environment | the dice game + the opponent |
| State | my 2 dice + the bids made so far |
| Action | raise the bid, or call "LIAR!" |
| Reward | +1 if the agent wins the round, −1 if it loses |
| Policy | the learned strategy: state → which action |
| Self-play | the agent plays itself thousands of times |
The learning rule (the REINFORCE algorithm): after each game, make the winner's moves more likely and the loser's moves less likely. Repeat.
| File
Liar's Dice by self-play — a Deep Reinforcement Learning teaching case study. An AI learns to bluff from scratch in PyTorch, playable in the browser.
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