Little Sorry

A Rust library for regret minimization algorithms (Counterfactual Regret Minimization) used to find Nash equilibrium strategies in imperfect-information games.

Features

6 CFR variants via the RegretMinimizer trait:
- CFR+ — regret clipping at zero
- Discounted CFR (DCFR) — time-based discounting with configurable parameters
- DCFR+ — combines DCFR discounting with CFR+ clipping
- Linear CFR — linear time-weighted regrets
- Predictive CFR+ (PCFR+) — uses future regret predictions
- Predictive DCFR+ (PDCFR+) — combines DCFR+ discounting with predictive updates
Zero-allocation hot path — no heap allocations during update_regret
Minimal dependencies (rand only)
Rock-Paper-Scissors example game (feature-gated behind rps)

Getting Started

Add this to your Cargo.toml:

[dependencies]
little-sorry = "3.0.0"

Quick Example

use little_sorry::{CfrPlusRegretMatcher, RegretMinimizer};

let mut matcher = CfrPlusRegretMatcher::new(3);

// Run many iterations of regret updates
for _ in 0..1000 {
    let rewards = &[1.0, -0.5, 0.2];
    matcher.update_regret(rewards);
}

// Get the Nash equilibrium approximation
let strategy = matcher.best_weight();

All variants implement the RegretMinimizer trait, so you can swap algorithms generically:

use little_sorry::{DiscountedRegretMatcher, RegretMinimizer};

fn train<M: RegretMinimizer>(matcher: &mut M, iterations: usize) {
    for _ in 0..iterations {
        let rewards = &[1.0, -0.5, 0.2];
        matcher.update_regret(rewards);
    }
}

Building and Testing

This project uses mise to manage tooling and tasks.

# Run all checks (formatting, linting, tests, TOML validation)
mise check

# Run tests
mise run check:test:nextest

# Run benchmarks
cargo bench --features rps

# Run the RPS example
cargo run --release --features rps --bin run_rps

License