Materials for AlphaGo
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Updated
Mar 10, 2022
Materials for AlphaGo
Congratulation to DeepMind! This is a reengineering implementation (on behalf of many other git repo in /support/) of DeepMind's Oct19th publication: [Mastering the Game of Go without Human Knowledge]. The supervised learning approach is more practical for individuals. (This repository has single purpose of education only)
Deep Reinforcement Learning algorithms to play Connect4 using a combination of Supervised Learning and Reinforcement Learning
Replicating AlphaGo's architecture in a readable manner
Naive Implementation to mimic AlphaGo next move.
An asynchronous implementation of AlphaZero, a self-play reinforcement learning algorithm.
Chess reinforcement learning by AlphaGo Zero methods.
Congratulation to DeepMind! This is a reengineering implementation (on behalf of many other git repo in /support/) of DeepMind's Oct19th publication: [Mastering the Game of Go without Human Knowledge]. The supervised learning approach is more practical for individuals. (This repository has single purpose of education only)
My implementation of alphazero algorithm for several games. (TicTacToe, Connect4, Gomoku). It can be extended to other games. Also implement a simple minmax Agent to compete against "alphazero" Agent.
Just another Hearthstone Simulator in C# .Net Core, with some A.I. approaches!
In this project, we combine AlphaGo algorithm with Curriculum Learning to crack the game of Tictactoe
Exercises from IT3105 V20
Reinforcement Learning for Sternhalma Plus AI Player
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