Hierarchical and Continual RL with Doina Precup - #567

This article summarizes a podcast episode featuring Doina Precup, a prominent researcher in reinforcement learning (RL). The discussion covers her research interests, including hierarchical reinforcement learning (HRL) for abstract representation learning, reward specification for intuitive intelligence, and her award-winning paper on Markov Reward. The episode also touches upon the analogy between HRL and CNNs, continual RL, and the evolution and challenges of the RL field. The focus is on Precup's contributions and insights into the current state and future directions of RL research.

• •Doina Precup's research focuses on hierarchical reinforcement learning for abstract representation learning.
• •She explores reward specification to enable agents to develop intuitive intelligence.
• •The episode covers her work on continual RL and the challenges facing the RL field.