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Testpyramidsrnd

Developed by SusBioRes-UBC
This is a reinforcement learning agent based on the PPO algorithm, specifically trained for navigation and task completion in Unity ML-Agents' pyramid environment.
Molecular Model
TensorBoard
#Unity Reinforcement Learning#3D Environment Navigation#PPO Algorithm Implementation
Downloads 16
Release Time : 7/10/2022

Model Overview

The model is trained using the Proximal Policy Optimization (PPO) algorithm within Unity's ML-Agents framework, aimed at solving navigation and decision-making tasks in the pyramid environment.

Model Features

Based on PPO Algorithm
Trained using the advanced reinforcement learning algorithm Proximal Policy Optimization, balancing exploration and exploitation.
Unity Environment Integration
Specifically designed for Unity ML-Agents' pyramid environment, seamlessly integrable into Unity projects.
Continuous Learning Capability
Supports resuming training via the --resume parameter, enabling continuous model improvement.

Model Capabilities

Environment Navigation
Path Planning
Decision Making
Reinforcement Learning

Use Cases

Game AI
Intelligent NPC Navigation
Provides intelligent navigation capabilities for NPCs in game environments.
NPCs can autonomously find paths and complete tasks.
Robot Simulation
Robot Path Planning
Provides environment exploration and path planning capabilities for simulated robots.
Robots can effectively navigate in complex environments.
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