Hierarchical and Continual RL with Doina Precup - #567
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Navigating Continual Reinforcement Learning
This chapter explores the concept of continual reinforcement learning, contrasting it with traditional methods confined to finite states and actions. It delves into the challenges faced by agents as they adapt to dynamic environments, emphasizing sample complexity, regret, and effective exploration strategies. Additionally, the chapter examines the relationship between continual reinforcement learning and time series problems, using the stock market as a key example to illustrate decision-making over time.
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