Machine Learning / AI
Lunar Lander RL Agent
An autonomous AI agent trained using Deep Reinforcement Learning (PPO) to safely land a spacecraft in a physics-based simulation.
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The Problem
It solves the complex control theory problem of stabilizing and landing a rocket in a continuous state space.
Target Audience
AI/ML enthusiasts, developers interested in autonomous systems and reinforcement learning
Project Details
It demonstrates how Reinforcement Learning agents can learn optimal control policies from scratch without hard-coded rules.