The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Trends in Reinforcement Learning with Pablo Samuel Castro - #443
Dec 30, 2020
Pablo Samuel Castro, a Staff Research Software Developer at Google Brain, joins for a deep dive into the evolving world of reinforcement learning. He discusses the latest advancements from major conferences, highlighting key themes like the integration of deep learning and real-world applications. The conversation touches on contrastive loss, the importance of small environments for research, and innovative solutions for disaster connectivity using RL and loon balloons. Expect insights on performance evaluation and the future landscape of deep reinforcement learning.
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DeepRL's Theoretical Gap
- Deep reinforcement learning (DeepRL) lacks theoretical understanding beyond linear function approximation.
- This poses challenges for real-world applications requiring reliability and explainability.
DeepRL's Dynamic Complexity
- DeepRL's dynamic data collection, influenced by agent behavior, makes it harder to analyze than supervised learning.
- Bootstrapping further complicates theoretical analysis due to error propagation.
Representations and Generalization
- Good representations in DeepRL group states with similar optimal actions, aiding generalization.
- Metrics like behavioral distance can guide representation learning by grouping states with similar transition dynamics and rewards.