GIANT ‐ Evaluation Environment - UM-LPM/GIANT GitHub Wiki

An evaluation environment is a controlled environment designed to assess AI agents in a structured and repeatable manner. These environments provide realistic or abstract simulations where agents can interact, learn, and be evaluated based on their performance.

For AI research and development, an evaluation environment is essential for:

  • Testing AI behavior in different conditions.
  • Benchmarking performance across various AI techniques.
  • Training reinforcement learning models through trial and error.
  • Comparing multiple AI controllers to determine the most effective approach.

Whether the goal is game AI, robotics, or autonomous decision-making, an evaluation environment serves as the foundation for developing intelligent and adaptive systems.

The Role of Evaluation Environments in AI Development

AI agents require structured environments where their decisions can be observed, measured, and improved. These environments act as testing grounds where different AI controllers can be compared in a consistent and objective way.

An effective evaluation environment should provide:

  • A Simulated World – A digital space where AI agents operate.
  • Defined Rules & Constraints – Clear guidelines for how AI interacts with the world.
  • Observation & Action Spaces – Mechanisms for AI to perceive its surroundings and take actions.
  • Performance Metrics – Methods to evaluate AI effectiveness based on predefined objectives.

For example, in a game AI scenario, an evaluation environment might involve a virtual arena where AI-controlled agents compete, cooperate, or solve challenges. Their success can be measured by factors like win rate, survival time, resource collection efficiency, or adaptability to different opponents.

Why Use an Existing Evaluation Environment?

While it’s possible to create custom evaluation environments from scratch for testing and training AI agents, using an existing evaluation environment offers several key advantages:

  1. Time and Cost Efficiency:

    Building an evaluation environment from the ground up can be time-consuming and resource-intensive. By leveraging existing ones, you can focus on developing and testing AI models without worrying about the underlying infrastructure.

  2. Proven and Reliable Frameworks:

    Established evaluation environments have undergone rigorous testing and refinement, because it was previously used by many other game developers, researcher and other who wanted to explore the AI for autonomous control.

  3. Optimized for AI Training:

    Many existing evaluation environments are specifically designed to support AI training, with built-in features like simulation speed, parallel processing, and customizable agent behaviors. These features are crucial for effectively training reinforcement learning models, especially when large-scale evaluations are necessary.

  4. Standardization and Benchmarking:

    Using an existing evaluation environment ensures consistency in testing and allows for standardized benchmarks. This is essential for comparing different AI approaches in a fair and objective manner. Existing platforms often come with pre-designed problem domains and simulation scenarios, making it easy to evaluate performance across various models.

  5. Community Support and Development:

    Popular evaluation environments have large user communities and active development teams. This support ensures regular updates, bug fixes, and feature expansions, giving developers access to the latest advancements in AI testing. Additionally, many platforms offer tutorials, documentation, and sample projects to help you get started quickly.

By using an existing evaluation environment, you gain the ability to accelerate AI development, leverage community-driven improvements, and ensure that your evaluations are robust and repeatable. This reduces the complexity of the process and allows you to focus on refining and optimizing your AI agents.

Summary

Currently, the GIANT platform supports a single evaluation environment, which is based on the powerful Unity Game Engine. Unity has been chosen due to its versatility, scalability, and extensive support for both 2D and 3D environments, making it an ideal choice for simulating various problem domains for AI training and evaluation.