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Ppo LunarLander V2
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Ppo LunarLander V2

Developed by sigalaz
This is a reinforcement learning model based on the PPO algorithm, designed to solve control tasks in the LunarLander-v2 environment.
Physics Model#Lunar Lander Control#Deep Reinforcement Learning#Stable Training
Downloads 20
Release Time : 7/8/2022

Model Overview

The model is trained using the Proximal Policy Optimization (PPO) algorithm in the LunarLander-v2 environment and can learn how to safely control a spacecraft for landing.

Model Features

Based on PPO Algorithm
Uses the Proximal Policy Optimization algorithm, an advanced reinforcement learning algorithm known for stable training characteristics.
Continuous Action Space Handling
Capable of handling control problems in continuous action spaces, making it suitable for precision tasks like spacecraft landing.
Stable Training
The PPO algorithm is designed to reduce the magnitude of policy updates during training, ensuring training stability.

Model Capabilities

Spacecraft Control
Continuous Action Decision-Making
Reinforcement Learning Task Solving

Use Cases

Space Simulation
Lunar Lander Control
Simulates the process of safely landing a spacecraft on the lunar surface.
Average reward reaches 274.78 +/- 19.67
Educational Demonstration
Reinforcement Learning Teaching
Serves as a classic case study for teaching reinforcement learning algorithms.
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