Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

🚀 A very efficient Texas Holdem GTO solver :spades::hearts::clubs::diamonds:

An awesome curated list of Cryptoeconomic research and learning materials

A research tool for the Iterated Prisoner's Dilemma

Shapley Interactions and Shapley Values for Machine Learning

[Development suspended] Advanced open-source Texas Hold'em GTO solver with optimized performance (web browser version)

Gambit: The package for computation in game theory

Code and datasets for the Tsetlin Machine

Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"

An open-source Python library for poker game simulations, hand evaluations, and statistical analysis

Game Theory Cheat Sheet

[Development suspended] An efficient open-source postflop solver library written in Rust

Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks

[Development suspended] Advanced open-source Texas Hold'em GTO solver with optimized performance

Collection of articles, books, videos and other things I found useful for those interested in the topic.

Official implementation of "Multi-Task Learning as a Bargaining Game" [ICML 2022]

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

A collection of classical algorithms and data-structures implementation in C++ for coding interview and competitive programming

Game Theory and Python, a workshop investigating repeated games using the prisoner's dilemma

Iterative Linear-Quadratic Games!

The All ▲lgorithms documentation website.

An Awesome list for resources on algorithmic game theory

A package for solving matching games

The decision-making of multiple vehicles at intersection bases on level-k game and MCTS

pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation

A research platform to develop automated security policies using quantitative methods, e.g., optimal control, computational game theory, reinforcement learning, optimization, evolutionary methods, and causal inference.

A course on imprecise probabilistic machine learning

Collection of papers in the field of distributed systems, game theory, cryptography, cryptoeconomics, zero knowledge

The materials used in the Polkadot Blockchain Academy

Code for KDD 2020 paper Robust Spammer Detection by Nash Reinforcement Learning