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16min chapter
David Silver 2 - Discussion after Keynote @ RCL 2024
TalkRL: The Reinforcement Learning Podcast
Exploring Meta-Learning and Planning in Reinforcement Learning
This chapter examines the interaction between function approximators and meta-learning mechanics, focusing on how internal planning algorithms can improve reinforcement learning during inference. It also compares human cognition to machine learning capabilities in addressing complex problem-solving scenarios.
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