Chapter
Reinforcement Learning and its Practical Applications
19:09 - 23:40 (04:31)
The lack of practical applications of reinforcement learning during the author's time at Stanford inspired him to seek ways to create work in the area that would have a positive impact and influence people. This mindset led to the successful development of the autonomous helicopter work for flying helicopters using reinforcement learning.
Clips
Researchers Peter Abbeel and John Schulman discuss their use of reinforcement learning to make a helicopter fly upside down and do stunts, including the challenges they faced with GPS units and positioning.
19:09 - 20:40 (01:31)
Summary
Researchers Peter Abbeel and John Schulman discuss their use of reinforcement learning to make a helicopter fly upside down and do stunts, including the challenges they faced with GPS units and positioning.
ChapterReinforcement Learning and its Practical Applications
Episode#73 – Andrew Ng: Deep Learning, Education, and Real-World AI
PodcastLex Fridman Podcast
The autonomous helicopter work for flying helicopters was one of the few practical applications of reinforcement learning, which made it popular.
20:40 - 23:40 (03:00)
Summary
The autonomous helicopter work for flying helicopters was one of the few practical applications of reinforcement learning, which made it popular. It solved the localization problem and helped focus on reinforcement learning and inverse reinforcement learning techniques.