Play Unity game with ML-powered agents
https://huggingface.co/spaces/VIDraft/mouse-webgen
Simulate vehicle physics with WASD controls
Play a Unity-based block pushing game
Play the Space Escape game
Play a physics-based vehicle simulation using WASD controls
https://huggingface.co/spaces/VIDraft/mouse-webgen
Play web-based vehicle physics simulations using WASD controls
Play a water-filling game
Run an interactive Unity game
Type any content you'd like, and the AI will generate it!
Drive a vehicle using keyboard input
Control virtual vehicles with WASD
Unity MLAgents Pyramids is a tool within the Unity MLAgents framework designed to enable the creation and training of AI-powered agents in Unity games. It allows developers to build intelligent in-game characters that can learn and adapt through interactions with the environment. Pyramids provides a simplified interface for setting up and training machine learning models directly within Unity projects.
• ML-Based Decision Making: Agents can make decisions autonomously using machine learning algorithms.
• Integrated with Unity MLAgents: Pyramids is built on top of Unity's MLAgents framework, offering seamless integration.
• Customizable Environments: Easily create custom game environments for training agents.
• Efficient Training: Designed to handle large-scale training with optimal performance.
• Built-In Reward System: Define reward structures to guide agent behavior during training.
• Cross-Platform Support: Works across Unity's supported platforms, including mobile and desktop.
What is the purpose of Unity MLAgents Pyramids?
Unity MLAgents Pyramids simplifies the process of creating and training AI-powered agents in Unity games, allowing developers to easily implement machine learning-based behaviors.
Can I customize the behavior of the agents?
Yes, Unity MLAgents Pyramids allows developers to fully customize the behavior and training parameters of agents, including reward structures and decision-making logic.
How do I train an agent using Pyramids?
Training an agent involves setting up the environment, defining the agent's behavior, and starting the training process through the Unity MLAgents interface. The agent learns by interacting with the environment and receiving rewards or penalties based on its actions.