Testpyramidsrnd
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for navigation and task completion in Unity's ML-Agents pyramid environment.
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Release Time : 8/20/2022
Model Overview
This model is trained using the Proximal Policy Optimization (PPO) algorithm in Unity ML-Agents' pyramid environment, capable of learning to navigate and solve tasks in complex 3D environments.
Model Features
Pyramid Environment Adaptation
Specially optimized and trained for Unity ML-Agents' pyramid 3D environment
PPO Algorithm Implementation
Utilizes the Proximal Policy Optimization algorithm to balance exploration and exploitation for stable learning
Real-time Demo Support
Supports real-time visual demonstrations via Hugging Face Spaces
Model Capabilities
3D Environment Navigation
Obstacle Avoidance
Goal-Oriented Behavior
Reinforcement Learning Decision Making
Use Cases
Game AI
NPC Intelligent Navigation
Used for autonomous navigation of NPCs in complex 3D game environments
Robot Simulation
Robot Path Planning
Simulates a robot's navigation capabilities in complex environments
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