Cybernetic1/reinforcement-learning-experiments

Reinforcement learning experiments with Tic Tac Toe, especially with "logical representations"

18
/ 100
Experimental

This project helps researchers and AI practitioners experiment with reinforcement learning (RL) algorithms for logical reasoning tasks, specifically using Tic-Tac-Toe as a demonstration. It takes in various RL configurations, including policy gradient and Q-learning with different neural network architectures, and outputs performance metrics and visualizations that show how well an AI learns to play the game.

Use this if you are an AI researcher or practitioner looking to understand and compare different reinforcement learning approaches, especially those using symmetric neural networks or transformers for problems involving sequential decision-making and logical states.

Not ideal if you are looking for a plug-and-play solution to integrate a Tic-Tac-Toe AI into an application or if you need robust, production-ready RL implementations.

reinforcement-learning-research AI-experimentation game-AI neural-network-design logical-reasoning
No License No Package No Dependents
Maintenance 6 / 25
Adoption 4 / 25
Maturity 8 / 25
Community 0 / 25

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Language

Python

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Last pushed

Dec 03, 2025

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