MOReL: Model-Based Offline Reinforcement Learning with Aravind Rajeswaran - #442
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Stateful MDPs and Offline RL Insights
This chapter explores the foundational concepts of stateful Markov Decision Processes (MDPs) in the context of offline reinforcement learning. It examines the importance of compact state representations, the explore-exploit trade-off, and the necessity of safety in real-world applications. Additionally, the chapter discusses a novel approach to offline RL that incorporates pessimistic biases in model dynamics, enhancing performance by encouraging agents to operate within known regions.
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