Today we close out our NeurIPS series joined by Aravind Rajeswaran, a PhD Student in machine learning and robotics at the University of Washington.
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At NeurIPS, Aravind presented his paper “MOReL: Model-Based Offline Reinforcement Learning.” In our conversation, we explore model-based reinforcement learning and if models are a “prerequisite” to achieve something analogous to transfer learning. We also dig into MOReL and the recent progress in offline reinforcement learning, the differences in developing MOReL models and traditional RL models, and the theoretical results they’re seeing from this research.
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I’d like to send a huge thank you to our friends at Qualcomm Technologies for their continued support of the podcast, and their sponsorship of this NeurIPS series! 
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Connect with Aravind
Resources
- Paper: MOReL: Model-Based Offline Reinforcement Learning
- Relational, Object-Centric Agents for Completing Simulated Household Tasks with Wilka Carvalho – #402
- Advances in Reinforcement Learning with Sergey Levine – #355
- Trends in Reinforcement Learning with Chelsea Finn
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“More On That Later” by Lee Rosevere licensed under CC By 4.0
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