The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Inverse Reinforcement Learning Without RL with Gokul Swamy - #643
Aug 21, 2023
Gokul Swamy, a Ph.D. student at Carnegie Mellon’s Robotics Institute, dives into the intriguing world of inverse reinforcement learning. He unpacks the challenges of mimicking human decision-making without direct reinforcement signals. Topics include streamlining AI learning through expert guidance and the complexities of medical decision-making with missing data. Gokul also discusses safety in multitask learning, emphasizing the balance between efficiency and safety in AI systems. His insights pave the way for future research in enhancing AI’s learning capabilities.
33:55
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Quick takeaways
- Inverse reinforcement learning can be made more efficient by focusing on relevant states and reducing unnecessary exploration.
- Shared safety constraints can be learned from multitask demonstrations, inferring actions that consistently absent in the demonstrations.
Deep dives
Efficient, Interactive Learning and Decision Making
The podcast episode features Gokko Swami, a PhD student at the Robotics Institute at Carnegie Mellon University. He discusses his research in the fields of efficient, interactive learning and making decisions without observable boundaries. His focus is on developing algorithms for imitation learning, specifically in the context of self-driving cars. He explores the challenges of efficient learning and dealing with partial observability in decision-making processes. Gokko highlights the importance of developing algorithms that can learn well while reducing the amount of data and computation needed. He also discusses the applications of his research, such as self-driving cars and large language models.
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