Popular GitHub Repositories for Quasi Monte Carlo

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

Optimizing inference proxy for LLMs

MuZero

Monte Carlo tree search in JAX

Bayesian inference with probabilistic programming.

Gaussian processes in TensorFlow

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

Differentiable rendering without approximation.

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

A tool for retrosynthetic planning

A curated list of Monte Carlo tree search papers with implementations.

Machine learning algorithms for many-body quantum systems

Diffusion Classifier leverages pretrained diffusion models to perform zero-shot classification without additional training

DGMs for NLP. A roadmap.

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

A Hearthstone AI based on Monte Carlo tree search and neural nets written in modern C++.

[NeurIPS 2023] We use large language models as commonsense world model and heuristic policy within Monte-Carlo Tree Search, enabling better-reasoned decision-making for daily task planning problems.