Play Unity game with ML-powered agents
Run an interactive Unity game
Simulate neuroevolution to train flappy birds
Play Chess with AI
https://huggingface.co/spaces/VIDraft/mouse-webgen
Explore a pyramid-solving game with AI
Control a physics vehicle in a web-based simulation
Control a vehicle in a physics-based simulation using WASD
Load and play a Unity web game
Simulate a Faker match
Control a simulated vehicle with WASD
Play an interactive Unity game
Find neighboring countries in a fun quiz game!
Unity MLAgents Pyramids is a tool within the Unity ML-Agents framework designed for training and testing AI agents in pyramid-building scenarios. It allows developers to leverage machine learning (ML) to create autonomous agents that can interact with and manipulate objects in a Unity environment. This tool simplifies the process of integrating ML into Unity games, enabling agents to learn through reinforcement learning and other AI techniques.
• ML-Powered Agents: Train agents to perform tasks using reinforcement learning and imitation learning.
• Pyramid-Building Environment: A pre-built environment where agents can practice stacking objects to form pyramids.
• Customizable Rewards: Define specific reward functions to guide agent behavior and optimize learning.
• Physics-Based Interactions: Utilizes Unity's physics engine for realistic object interactions.
• Real-Time Visualization: Observe agent behavior and training progress directly within the Unity Editor.
• Benchmarking: Compare performance across different training configurations and algorithms.
What platforms does Unity MLAgents Pyramids support?
Unity MLAgents Pyramids is primarily designed for Windows, macOS, and Linux, as it relies on the Unity Editor and Python ecosystem.
Can I customize the pyramid-building environment?
Yes, the environment is fully customizable. You can modify object sizes, colors, and physics properties to suit your specific use case.
How does Unity MLAgents Pyramids differ from other ML-Agents demos?
Unity MLAgents Pyramids is specifically optimized for object manipulation and stacking tasks, providing a unique environment for training agents in precision-based tasks.