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Mlagents Crawler
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Mlagents Crawler

Developed by infinitejoy
This is a PPO agent model trained using the Unity ML-Agents library, specifically designed for reinforcement learning tasks in the Crawler environment.
Molecular Model
TensorBoard
#Multi-legged Robot Control#PPO Reinforcement Learning#Unity Simulation Training
Downloads 21
Release Time : 7/16/2022

Model Overview

This model is a reinforcement learning agent trained based on the PPO (Proximal Policy Optimization) algorithm, used for controlling the movement of multi-legged robots in the Crawler environment.

Model Features

Based on PPO Algorithm
Trained using the Proximal Policy Optimization algorithm, a stable and efficient reinforcement learning method.
Unity ML-Agents Integration
Fully compatible with the Unity ML-Agents library, allowing easy deployment in Unity environments.
Multi-legged Robot Control
Specifically designed for controlling the complex movements of multi-legged robots in the Crawler environment.

Model Capabilities

Multi-legged Robot Motion Control
Reinforcement Learning Decision Making
Continuous Action Space Handling

Use Cases

Robot Control
Multi-legged Robot Walking
Control multi-legged robots to walk stably in complex environments
The model can learn effective gait strategies
Reinforcement Learning Research
Serves as a benchmark environment for PPO algorithm testing
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