ppo-Pendulum-v1 Open-source Reinforcement Learning Model - Free Deployment to Solve Control Problems

Ppo Pendulum V1

Developed by ernestumorga

This is a reinforcement learning model based on the PPO algorithm, designed to solve control problems in the Pendulum-v1 environment.

Physics Model #Continuous Control #Physics Simulation #Efficient Training

Downloads 16

Release Time : 6/7/2022

Model Overview

The model is trained using the PPO (Proximal Policy Optimization) algorithm in the Pendulum-v1 environment, aiming to achieve stable control of an inverted pendulum.

Model Features

Based on PPO Algorithm

Trained using the PPO algorithm, an advanced policy optimization method that ensures training stability while achieving efficient learning.

Multi-environment Parallel Training

Supports training in 4 parallel environments (n_envs=4), improving training efficiency.

State-Dependent Exploration

Uses State-Dependent Exploration (use_sde=True) to enhance exploration capabilities.

Model Capabilities

Inverted Pendulum Control

Continuous Action Space Handling

Reinforcement Learning Policy Optimization

Use Cases

Control Problems

Inverted Pendulum Balance Control

Control the inverted pendulum to maintain an upright position

Average reward: -227.99 +/- 144.65

Property	Details
Model Type	PPO
Training Data	Pendulum-v1
Mean Reward	-227.99 +/- 144.65
Task	Reinforcement Learning

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Ppo Pendulum V1

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Stable-Baselines3 PPO Agent for Pendulum-v1

🚀 Quick Start

📦 Installation

💻 Usage Examples

Basic Usage

Training

🔧 Technical Details

Hyperparameters

📚 Documentation

Model Performance