npnpwqf / Renju
基于强化学习的五子棋
☆11Updated 6 years ago
Alternatives and similar repositories for Renju:
Users that are interested in Renju are comparing it to the libraries listed below
- 用深度学习+强化学习编写的一个五子棋人工智障☆41Updated 7 years ago
- 使用浅层神经网络和遗传算法训练一个可以自动驾驶小车的Demo☆84Updated 5 years ago
- 国立台湾大学李宏毅老师讲解的深度强化学习学习笔记☆142Updated 5 years ago
- Here are some Python implementations of Gomoku AIs, including MCTS, Minimax and Genetic Alg.☆31Updated 6 years ago
- A pytorch based Gomoku game model. Alpha Zero algorithm based reinforcement Learning and Monte Carlo Tree Search model.☆166Updated 6 years ago
- 深度强化学习贪吃蛇游戏。拥有 完整游戏环境与AI接口。(项目未完成)☆37Updated 5 years ago
- 本论文题目为基于深度强化学习的德州扑克AI算法优化☆24Updated 4 years ago
- AI项目(强化学习、深度学习、计算机视觉、推荐系统、自然语言处理、机器导航、医学影像处理)☆87Updated last year
- 用强化学习DQN算法,训练AI模型来玩合成大西瓜游戏,提供Keras版本和PARL(paddle)版本☆89Updated 4 years ago
- 强化学习训练斗地主 / doudizhu AI using reinforcement learning.☆15Updated 5 years ago
- 天授中文文档☆58Updated 4 months ago
- The AlphaZero for the WTN-EinStein Chess☆6Updated 6 years ago
- 强化学习求解迷宫问题,Q-learning和监督学习☆25Updated 4 years ago
- 强化学习经典算法(offline\online learning, q-learning, DQN)的实现在平衡杆游戏和几个Atari 游戏 (CartPole\Pong\Boxing\MsPacman)☆29Updated 6 years ago
- 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☆97Updated 2 years ago
- 🎲 又是一个黑白棋,毕业设计(reinforcement learning)☆75Updated 4 years ago
- 《算法笔记》(胡凡,曾磊主编)作业☆30Updated 5 years ago
- 机器学习项目☆32Updated 6 years ago
- 白话强化学习与PyTorch的学习笔记☆35Updated 5 years ago
- 《强化学习-原理与Python实现》的Pytorch实现。☆59Updated 4 years ago
- 使用pytorch构建深度强化学习模型DQN☆24Updated 7 years ago
- 强化学习-中文笔记&资源-以python实例为主-由浅入深☆100Updated 4 years ago
- Tutorial4RL: Tutorial for Reinforcement Learning. 强化学习入门教程.☆144Updated last year
- 这是一个学习强化学习基础原理的仓库,主要包括了《深入浅出强化学习原理入门》书中一些例子和课后作业的代码☆260Updated 6 years ago
- 基于DQN的五子棋人机对弈☆58Updated 6 years ago
- 用强化学习玩俄罗斯方块☆18Updated 7 years ago
- Connect6 AI based on AlphaZero☆22Updated 6 years ago
- [动手学强化学习]系列,基于pytorch。☆54Updated 3 years ago
- ☆90Updated 2 years ago
- An AI program to play Othello game. Use DQN, Double-DQN, Dueling DQN and MCTS. By Chengzhe XU and Hao XIANG☆8Updated 7 years ago