PPO-LunarLander-v2 Open Source Reinforcement Learning Model - Free Deployment to Solve Lunar Landing Task

Ppo LunarLander V2

Developed by tooalvin

This is a reinforcement learning model based on the PPO algorithm, specifically designed to solve the landing task in the LunarLander-v2 environment.

Downloads 13

Release Time : 2/10/2025

Model Overview

The model is trained using the Proximal Policy Optimization (PPO) algorithm, aiming to safely control the spacecraft's landing on the lunar surface.

Stable Training

Uses the PPO algorithm to ensure training stability.

Continuous Action Space Handling

Capable of handling continuous action spaces in the LunarLander environment.

Reward Optimization

Optimizes the spacecraft's landing reward function through reinforcement learning.

Spacecraft Control

Continuous Action Decision-Making

Reinforcement Learning Task Solving

Space Simulation

Lunar Lander Control

Simulates the process of controlling a spacecraft to land safely on the lunar surface.

Average reward reaches 92.08 +/- 122.82

Educational Demonstration

Reinforcement Learning Teaching Case

Serves as a teaching demonstration case for reinforcement learning algorithms.

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