ml-agents and Self-Play-TicTacToe-AI-ML-Agents-
ML-Agents is a foundational reinforcement learning framework that Self-Play-TicTacToe-AI uses as its core dependency to implement a specific game-playing agent, making them ecosystem siblings where one is the general-purpose toolkit and the other is an example application built on top of it.
About ml-agents
Unity-Technologies/ml-agents
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Implements PyTorch-based algorithms (PPO, SAC, MA-POCA) for single and multi-agent scenarios, with support for imitation learning, curriculum learning, and environment randomization. Provides native cross-platform inference through its Inference Engine and exposes Unity environments as standard Python APIs (Gym, PettingZoo) for seamless integration with the broader ML ecosystem. Enables on-demand agent decision-making and concurrent training across multiple environment instances for scalable experiment iteration.
About Self-Play-TicTacToe-AI-ML-Agents-
Sebastian-Schuchmann/Self-Play-TicTacToe-AI-ML-Agents-
A Self Play reinforcement learning Agent learns to play TicTacToe using the ML-Agents Framework in Unity.
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