✈️Training an AI to fly requires using reinforcement learning and proximal policy optimization.
📊Training the AI involved setting up a reward system and milestones to track progress.
🔧The AI was trained using Unity's PPO algorithm and a neural network.
🎮The AI was able to hover around a single point, navigate to different targets, and fly through an obstacle course.
📈The AI's flying abilities improved over time, showcasing the power of machine learning.
What is reinforcement learning?
—Reinforcement learning is a machine learning technique where an agent learns to optimize its behavior by interacting with an environment and receiving rewards or punishments based on its actions.
What is proximal policy optimization?
—Proximal policy optimization (PPO) is an algorithm used to train reinforcement learning models. It helps optimize policies by balancing exploration and exploitation.
How was the AI trained to fly?
—The AI was trained using Unity's PPO algorithm and a neural network. A reward system was set up, and milestones were defined to track progress.
What were the milestones in the training process?
—The milestones included hovering around a single point, navigating to different targets, and eventually flying through an obstacle course.
Did the AI improve its flying abilities over time?
—Yes, the AI showed improvement in its flying abilities over time, demonstrating the effectiveness of machine learning in training complex tasks.
00:02The video introduces the journey of training an AI to fly using reinforcement learning and proximal policy optimization.
02:23The AI starts by learning to hover around a single point.
04:50The AI progresses to navigating different targets in the training environment.
06:56The AI attempts to fly through an obstacle course, showcasing improved flying abilities.
09:33The video discusses the next steps of training a quadcopter to compare its performance with the AI's flying abilities.
