Ian Osband
TalkRL: The Reinforcement Learning Podcast
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Balancing Exploration and Exploitation in Reinforcement Learning
This chapter explores the concept of information directed sampling and its role in navigating the trade-off between exploration and exploitation in reinforcement learning. It discusses approaches like Thompson sampling and deep exploration, emphasizing the importance of uncertainty and curiosity in efficiently solving complex problems.
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