gh877916059 / Reinforcement-learning-demos-annotated
使用OpenAI Gym实现游戏AI
☆16Updated 7 years ago
Related projects ⓘ
Alternatives and complementary repositories for Reinforcement-learning-demos-annotated
- 强化学习-游戏AI Trainning☆32Updated 5 years ago
- AlphaZero implemented Chinese chess. AlphaGo Zero / AlphaZero实践项目,实现中国象棋。☆483Updated last year
- AI斗地主☆184Updated 6 years ago
- [NeurIPS 2022] PerfectDou: Dominating DouDizhu with Perfect Information Distillation☆161Updated 6 months ago
- Resources of 3D Wizard Projects☆62Updated 3 years ago
- ☆59Updated 5 years ago
- This is the code of using machine learning to play Sekiro .☆96Updated 3 years ago
- A pytorch based Gomoku game model. Alpha Zero algorithm based reinforcement Learning and Monte Carlo Tree Search model.☆161Updated 5 years ago
- A tiny re-implementation of AlphaGo Zero (in Gomoku)☆69Updated 6 years ago
- Play flappy bird with DQN, a demo for reinforcement learning, implemented using PyTorch☆67Updated 7 years ago
- D3QN 强化学习打只狼☆21Updated 2 years ago
- 游戏AI的顶会论文总结与分类~还有游戏AI的基础问题与算法讨论。game AI paper list~☆122Updated 3 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)☆35Updated 5 years ago
- An asynchronous/parallel method of AlphaGo Zero algorithm with Gomoku☆189Updated 4 years ago
- 使用alphazero算法打造属于你自己的象棋AI☆217Updated 2 years ago
- ☆36Updated 4 years ago
- 中国象棋pygame☆56Updated 10 months ago
- ☆125Updated 3 years ago
- C++/python fight the lord with pybind11 (强化学习AI斗地主), Accepted to AIIDE-2020☆157Updated 3 years ago
- A student implementation of Alpha Go Zero☆279Updated 6 years ago
- An AlphaZero implementation of game Quoridor☆35Updated 4 years ago
- OpenAI Gym Env for game Gomoku(Five-In-a-Row, 五子棋, 五目並べ, omok, Gobang,...)☆85Updated last month
- Several board game AI, which are trained based on AlphaZero, used in a Unity game.☆39Updated 4 years ago
- 中国象棋alpha zero程序☆379Updated 5 years ago
- 基于DQN的五子棋人机对弈☆55Updated 5 years ago
- This work attempts to train AlphaZero agents on the game of Chain Reaction☆21Updated last year
- DQN_play_sekiro☆470Updated 2 months ago
- 该论文主要介绍了美国卡内基梅隆大学团队,在多人德州扑克上的人工智能新思路,即不再简单寻找纳什均衡,而引入悔恨值的概念,自我博弈,并采用蒙特卡洛CFR方法,构建蓝图,该方法通用性强,该团队声称他们的德州扑克蓝图只在两枚CPU运算8天即可得出蓝图,即可以实现实时博弈。现已经有国…☆25Updated 5 years ago
- ☆218Updated 5 years ago