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Deep Reinforcement Learning: Pong from Pixels

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Deep Reinforcement Learning: Pong from Pixels

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Summary

— 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 John Schulmann’s lectures , wrote an RL library in Javascript , over the summer interned at DeepMind working i…

What This Teaches

  • How Karpathy frames technical judgment, learning, research, or AI systems in long-form prose.
  • Useful as a high-signal idea source for research taste, project framing, and agent workflow design.

Why It Matters

Karpathy’s posts often crystallize reusable heuristics; this wiki should preserve the ideas without relying on chat memory.

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