Machine Learning Street Talk (MLST)

Prof. Jakob Foerster - ImageNet Moment for Reinforcement Learning?

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Feb 18, 2025
Guest
Jakob Foerster
Jakob Foerster, a prominent AI researcher at Oxford University and Meta, joins to discuss the future of AI. He emphasizes the shift from mimicking human behavior to developing intelligent agents that can learn independently. The conversation delves into the importance of open-source AI for responsible innovation and addresses challenges such as AI scaling and goal misalignment. They also explore advancements in deep reinforcement learning, the significance of creativity, and the need for democratization in AI to foster collaboration and mitigate risks.
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INSIGHT

Deep Reinforcement Learning's Hardware Bottleneck

  • Deep reinforcement learning's limited real-world impact is potentially due to a "hardware lottery" loss.
  • GPUs are suited to deep learning, but reinforcement learning often runs environments on CPUs, slowing progress.
ANECDOTE

FLAIR Lab's Early Compute Limitations

  • At FLAIR lab, initial reinforcement learning experiments were limited by compute resources.
  • Simple PyTorch environments on Google Colab were used, highlighting the effectiveness of GPU-based environments.
INSIGHT

JAX: A Tool for Reinforcement Learning

  • JAX, with its NumPy-like interface and JIT compilation, offers advantages for reinforcement learning.
  • Its VMAP feature simplifies running multiple environment instances concurrently on GPUs.
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