ml-agents and A.I.-Shooting-Game-ML-Agents-Unity-Example
ML-Agents is the core framework that Sebastian-Schuchmann's project extends as a beginner-friendly tutorial demonstrating practical implementation of the toolkit's reinforcement learning capabilities.
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 A.I.-Shooting-Game-ML-Agents-Unity-Example
Sebastian-Schuchmann/A.I.-Shooting-Game-ML-Agents-Unity-Example
A beginner friendly example for Unity's ML-Agents Framework. This project teaches you how to train an A.I. via Machine Learning.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work