Practical AI Deep Reinforcement Learning
Apr 23, 2019
Adam Stooke, a PhD student at UC Berkeley, dives into the fascinating world of deep reinforcement learning and robotics. He shares insights from his journey transitioning from physics to AI, emphasizing the trial-and-error nature of reinforcement learning. Adam discusses the impact of GPU computing, particularly the NVIDIA DGX1, on accelerating complex tasks like gaming. He highlights key advancements in the field from organizations like DeepMind and OpenAI, offering valuable advice for newcomers about the importance of hands-on experience.
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From Physics to AI
- Adam Stooke, a physics and math graduate, transitioned to AI.
- His Air Force experience with MATLAB and simulations sparked his interest in computer science and AI.
Atari Inspiration
- Adam's early interest in DRL was sparked by DeepMind's work on Atari games.
- He was inspired by agents learning to play from screen images, similar to humans.
Scaling DRL Implementations
- Explore deep reinforcement learning algorithms and focus on scaling implementations.
- Try to significantly reduce learning times by optimizing compute resource usage.