An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
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Updated
Apr 24, 2024 - Python
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
Easily train AlphaZero-like agents on any environment you want!
MCTS project for Tetris
A student implementation of Alpha Go Zero
A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku
Reinforcement learning models in ViZDoom environment
AlphaZero implementation for Othello, Connect-Four and Tic-Tac-Toe based on "Mastering the game of Go without human knowledge" and "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm" by DeepMind.
Reinforcing Your Learning of Reinforcement Learning
fast + parallel AlphaZero in JAX
Meta-Zeta是一个基于强化学习的五子棋(Gobang)模型,主要用以了解AlphaGo Zero的运行原理的Demo,即神经网络是如何指导MCTS做出决策的,以及如何自我对弈学习。源码+教程
Monte Carlo Tree Search (MCTS) is a method for finding optimal decisions in a given domain by taking random samples in the decision space and building a search tree accordingly. It has already had a profound impact on Artificial Intelligence (AI) approaches for domains that can be represented as trees of sequential decisions, particularly games …
基於深度學習的 GTP 圍棋(围棋)引擎,KGS 指引文件以及演算法教學。
Computer go engine using Monte-Carlo Tree Search written in Python3.
A number of agents (PPO, MuZero) with a Perceiver-based NN architecture that can be trained to achieve goals in nethack/minihack environments.
Deep Learning big homework of UCAS
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