PPO - LunarLander - v2 Open-source Reinforcement Learning Model, Free Deployment to Help Lunar Lander Land Safely

Ppo LunarLander V2

Developed by andri

This is a reinforcement learning model based on the PPO algorithm, specifically trained for the LunarLander-v2 environment to control the safe landing of a lunar lander.

Physics Model #Lunar Lander Control #Deep Reinforcement Learning #Stable Training Framework

Downloads 16

Release Time : 6/8/2022

Model Overview

This model is trained using the Proximal Policy Optimization (PPO) algorithm and can learn strategies to control the lunar lander in the LunarLander-v2 simulation environment to achieve a safe landing.

Model Features

Stable Training

Uses the PPO algorithm to provide a stable policy optimization process.

Efficient Learning

Can learn effective control strategies in relatively few training steps.

Reproducibility

Implemented based on stable-baselines3, ensuring good experimental reproducibility.

Model Capabilities

Reinforcement Learning Control

Continuous Action Space Handling

Environment State Perception

Use Cases

Game AI

Lunar Lander Control

Control the lander to land safely in the LunarLander-v2 environment.

Average reward reaches 263.23 +/- 15.11

Educational Demonstration

Reinforcement Learning Teaching

Serves as a classic case for teaching reinforcement learning algorithms.

Property	Details
Model Type	PPO
Mean Reward	263.23 +/- 15.11
Task	Reinforcement Learning
Dataset	LunarLander-v2

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Ppo LunarLander V2

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Stable-Baselines3 PPO Agent for LunarLander-v2

🚀 Quick Start

💻 Usage Examples

Basic Usage

📊 Model Metrics

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