AIbase
EN
Home
Assignment2 Omar
A

Assignment2 Omar

Developed by Classroom-workshop
This is a reinforcement learning model based on the PPO algorithm, specifically designed to solve the landing task in the LunarLander-v2 environment.
Physics Model#Lunar Lander Control#Deep Reinforcement Learning#Stable Training Framework
Downloads 135
Release Time : 6/2/2022

Model Overview

The model implements the PPO algorithm using the stable-baselines3 library, trained in the LunarLander-v2 environment with the goal of safely landing the lunar module.

Model Features

Stable Policy Optimization
Uses the PPO algorithm to achieve stable policy gradient updates, avoiding drastic fluctuations during training.
Continuous Action Space Support
Capable of handling continuous action space control problems in the LunarLander-v2 environment.
Efficient Learning
Compared to traditional reinforcement learning algorithms, PPO offers higher sample utilization efficiency.

Model Capabilities

Continuous Action Control
Reinforcement Learning Task Solving
Environment State Understanding
Policy Optimization

Use Cases

Game AI
Lunar Module Landing Control
Trains an AI agent to control the lunar module for safe landing in a designated area.
Average reward reaches 10 +/- 7.11
Educational Demonstration
Reinforcement Learning Teaching
Serves as a teaching example for the PPO algorithm, demonstrating the fundamentals of reinforcement learning.
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
English简体中文繁體中文にほんご
© 2025AIbase