no code implementations • 28 Feb 2024 • Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley Llorens, John Langford, Hoi Vo, Li Fei-Fei, Katsu Ikeuchi, Jianfeng Gao
Recent advancements in large foundation models have remarkably enhanced our understanding of sensory information in open-world environments.
no code implementations • 8 Feb 2024 • Zane Durante, Bidipta Sarkar, Ran Gong, Rohan Taori, Yusuke Noda, Paul Tang, Ehsan Adeli, Shrinidhi Kowshika Lakshmikanth, Kevin Schulman, Arnold Milstein, Demetri Terzopoulos, Ade Famoti, Noboru Kuno, Ashley Llorens, Hoi Vo, Katsu Ikeuchi, Li Fei-Fei, Jianfeng Gao, Naoki Wake, Qiuyuan Huang
We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks.
1 code implementation • 19 Aug 2022 • Jared Markowitz, Ryan W. Gardner, Ashley Llorens, Raman Arora, I-Jeng Wang
Without cost constraints, we find that pessimistic risk profiles can be used to reduce cost while improving total reward accumulation.
no code implementations • 29 Sep 2021 • Jared Markowitz, Ryan Gardner, Ashley Llorens, Raman Arora, I-Jeng Wang
Standard deep reinforcement learning (DRL) agents aim to maximize expected reward, considering collected experiences equally in formulating a policy.
no code implementations • 27 Oct 2020 • Ufuk Topcu, Nadya Bliss, Nancy Cooke, Missy Cummings, Ashley Llorens, Howard Shrobe, Lenore Zuck
The second workshop held in February 2020, focused on existing capabilities, current research, and research trends that could address the challenges and problems identified in workshop.