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LAD: Language Augmented Diffusion for Reinforcement Learning
Edwin Zhang · Yujie Lu · William Yang Wang · Amy Zhang
Event URL: https://openreview.net/forum?id=JhM3xZISMvN »

Learning skills from language potentially provides a powerful avenue for generalization in RL, although it remains a challenging task as it requires agents to capture the complex interdependencies between language, actions and states, also known as language grounding. In this paper, we propose leveraging Language Augmented Diffusion models as a language-to-plan generator (LAD). We demonstrate comparable performance of LAD with the state of the art on the CALVIN benchmark with a much simpler architecture and conduct an analysis on the properties of language conditioned diffusion in reinforcement learning.

Author Information

Edwin Zhang (UCSB)
Yujie Lu (University of California, Santa Barbara)
William Yang Wang (University of California, Santa Barbara)

William Wang is the Co-Director of UC Santa Barbara's Natural Language Processing group and Center for Responsible Machine Learning. He is the Duncan and Suzanne Mellichamp Chair in Artificial Intelligence and Designs, and an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara. He received his PhD from School of Computer Science, Carnegie Mellon University. He has broad interests in Artificial Intelligence, including statistical relational learning, information extraction, computational social science, dialog & generation, and vision. He has published more than 100 papers at leading NLP/AI/ML conferences and journals, and received best paper awards (or nominations) at ASRU 2013, CIKM 2013, EMNLP 2015, and CVPR 2019, a DARPA Young Faculty Award (Class of 2018), an IEEE AI's 10 to Watch Award (Class of 2020), an NSF CAREER Award (2021), two Google Faculty Research Awards (2018, 2019), three IBM Faculty Awards (2017-2019), two Facebook Research Awards (2018, 2019), an Amazon AWS Machine Learning Research Award, a JP Morgan Chase Faculty Research Award, an Adobe Research Award in 2018, and the Richard King Mellon Presidential Fellowship in 2011. He frequently serves as an Area Chair or Senior Area Chair for NAACL, ACL, EMNLP, and AAAI. He is an elected member of IEEE Speech and Language Processing Technical Committee (2021-2023) and a member of ACM Future of Computing Academy. In addition to research, William enjoys writing scientific articles that impact the broader online community. His work and opinions appear at major tech media outlets such as Wired, VICE, Scientific American, Fortune, Fast Company, NASDAQ, The Next Web, Law.com, and Mental Floss.

Amy Zhang (Facebook, UC Berkeley)

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