Emergent Behavior in RL
Agents trained in open-ended environments exhibit emergent behaviors, allowing them to generalize across unseen terrains and scenarios. Recent advancements have shifted focus towards applying deep reinforcement learning to complex real-world problems, such as protein folding and robotic manipulation. A notable trend involves reframing reinforcement learning as a supervised learning problem using transformers, which could pave the way for future breakthroughs in the field.In this clip
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Super Data Science: ML & AI Podcast with Jon Krohn
SDS 551: Deep Reinforcement Learning — with Wah Loon Keng
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