Simple reinforcement learning framework for selfplay experiments
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Updated
Apr 8, 2018 - Python
Simple reinforcement learning framework for selfplay experiments
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.
Implementation of an AlphaGo Zero paper in one C++ header file without any dependencies
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
A multi agent re-enforcement learning environment for many on many bot fights between space ships
A chess program based on Deep Mind's AlphaZero.
Using self-play, MCTS, and a deep neural network to create a hearthstone ai player
A AlphaZero implementation for Othello 10x10 / Reversi using C++
AlphaZero for ultimate tic-tac-toe.
Temporal difference learning for ultimate tic-tac-toe.
Backgammon OpenAI Gym
This is the implementation of paper Model Free Episodic Control
Implementation of Alpha Go Zero - Reinforcement Learning Project, COL870 @iit-delhi
Code repository for On the interaction between supervision and self-play in emergent communication (ICLR 2020)
A reinforcement learning agent trained without prior human knowledge
My attempt to reproduce a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning) using DDPPO (Decentralized & distributed proximal policy optimization) from ray[rllib].
An implementation of the AlphaZero algorithm for adversarial games to be used with the machine learning framework of your choice
Train a neural network to play 2048
The exact codes used by the team "liveinparis" at the kaggle football competition ranked 6th/1141
AI agents for the bavarian card game Schafkopf trained with reinforcement learning
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