A python implementaion of Counterfactual Regret Minimization using numba
-
Updated
Jan 12, 2022 - Python
A python implementaion of Counterfactual Regret Minimization using numba
This repository contains code for the paper "Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem".
This repository contains several implementations of multi-armed bandit (MAB) agents applied to a simulated cricket match where an agent selects among different strategies with the goal of maximizing runs while minimizing the risk of getting out.
Robust Deep Monte Carlo Counterfactual Regret Minimization: Addressing Theoretical Risks in Neural Fictitious Self-Play
Project on preference learning - ENSAE ParisTech
Source code for Regret synthesis for two-player turn-based game played on graphs - ICRA 22
Add a description, image, and links to the regret-minimization topic page so that developers can more easily learn about it.
To associate your repository with the regret-minimization topic, visit your repo's landing page and select "manage topics."