The General Intelligent AgeNt Trainer (GIANT) is an open-source project that provides a flexible architecture for training intelligent agents using various machine learning techniques in diverse simulation environments. The platform’s primary objective is to integrate advanced existing solutions, such as metaheuristic frameworks and game engines, to streamline the training and evaluation process.
The current implementation supports training with the Unity game engine and the EARS framework. Unity serves as an evaluation environment where solutions generated by the EARS framework are tested, while EARS functions as a metaheuristic framework that includes several validated optimization algorithms.
GIANT is designed with a highly modular structure, ensuring that each component operates independently and can be replaced or upgraded as needed. This makes the platform valuable for both researchers and game developers, providing tools for efficiently developing and evaluating new solutions.
- Four built-in example environments
- Support for both Single-Agent and Multi-Agent scenarios
- Highly extensible and modular design
- It is compatible with various decision-making systems, including Behavior Trees, Neural Networks, and more
- Multilevel parallelization
- Easily extendable to new problem domains
- Manual solution testing, allowing human players to compete against AI agents
- Visualization tools for comparing different methods
For a detailed description of these functionalities, see GIANT overview docs.
| Version | Release Date | Source (Git branch) | Docs | Download |
|---|---|---|---|---|
| Beta | Oktober 8, 2024 | -- | -- | download |
| Release 1 | April 2, 2025 | branch | docs | download |
For older releases, refer to the Releases page.
If you use GIANT, please include this citation as a reference to the platform.