PPO-LunarLander-v2 Open-Source Reinforcement Learning Model - Assist Moon Lander to Land Safely and Precisely

Ppo LunarLander V2

Developed by sofiascat

This is a reinforcement learning model based on the PPO algorithm, specifically trained for the LunarLander-v2 environment to safely control lunar landings.

Physics Model #Lunar Landing Control #Deep Reinforcement Learning #Stable Baselines3 Implementation

Downloads 14

Release Time : 4/12/2025

Model Overview

The model is trained using the Proximal Policy Optimization (PPO) algorithm in the LunarLander-v2 environment to solve continuous control problems, particularly spacecraft landing tasks.

Model Features

Stable Training

The PPO algorithm provides stable policy updates, avoiding drastic fluctuations during training.

Continuous Action Control

Capable of handling continuous action spaces, suitable for precise control tasks.

Efficient Learning

Achieves good performance with relatively few training steps.

Model Capabilities

Continuous Action Control

Reinforcement Learning Decision-Making

Spacecraft Landing Simulation

Use Cases

Space Simulation

Lunar Lander Control

Simulates controlling a lunar lander to safely touch down on the moon's surface.

Average reward reaches 263.22 +/- 22.53

Educational Demonstration

Reinforcement Learning Teaching

Serves as a classic case study for teaching reinforcement learning algorithms.

Property	Details
Model Type	PPO
Training Data	LunarLander-v2

Metric	Value
Mean Reward	263.22 +/- 22.53

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Ppo LunarLander V2

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Stable-Baselines3 PPO Agent for LunarLander-v2

🚀 Quick Start

Model Information

Metrics

💻 Usage Examples

Basic Usage