Deep Reinforcement Learning: Pong from Pixels

Musings of a Computer Scientist.

This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go , simulated quadrupeds are learning to run and leap , and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over the last ~year: I worked through Richard Sutton’s book , read through David Silver’s course , watched

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related reading

• Deep Reinforcement Learning: Pong from Pixelskarpathy.github.io
• A (Long) Peek into Reinforcement Learning | Lil'Loglilianweng.github.io
• The Unreasonable Effectiveness of Recurrent Neural Networkskarpathy.github.io
• A Recipe for Training Neural Networkskarpathy.github.io
• A Recipe for Training Neural Networkskarpathy.github.io
• Debugging Reinforcement Learning Systemsandyljones.com
• The 37 Implementation Details of Proximal Policy Optimization · The ICLR Blog Trackiclr-blog-track.github.io
• Deep Q-Networks Explained — LessWronglesswrong.com
• ML Mentorship: Some Q/A about RLevjang.com
• Deep Reinforcement Learning Doesn't Work Yetalexirpan.com
• An Overview of Deep Learning for Curious People | Lil'Loglilianweng.github.io
• Part 1: Key Concepts in RL - Spinning Up documentationspinningup.openai.com