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of 8
pro vyhledávání: '"Sarkar, Bidipta"'
Autor:
Huang, Qiuyuan, Wake, Naoki, Sarkar, Bidipta, Durante, Zane, Gong, Ran, Taori, Rohan, Noda, Yusuke, Terzopoulos, Demetri, Kuno, Noboru, Famoti, Ade, Llorens, Ashley, Langford, John, Vo, Hoi, Fei-Fei, Li, Ikeuchi, Katsu, Gao, Jianfeng
Recent advancements in large foundation models have remarkably enhanced our understanding of sensory information in open-world environments. In leveraging the power of foundation models, it is crucial for AI research to pivot away from excessive redu
Externí odkaz:
http://arxiv.org/abs/2403.00833
Autor:
Durante, Zane, Sarkar, Bidipta, Gong, Ran, Taori, Rohan, Noda, Yusuke, Tang, Paul, Adeli, Ehsan, Lakshmikanth, Shrinidhi Kowshika, Schulman, Kevin, Milstein, Arnold, Terzopoulos, Demetri, Famoti, Ade, Kuno, Noboru, Llorens, Ashley, Vo, Hoi, Ikeuchi, Katsu, Fei-Fei, Li, Gao, Jianfeng, Wake, Naoki, Huang, Qiuyuan
The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Mode
Externí odkaz:
http://arxiv.org/abs/2402.05929
Autor:
Durante, Zane, Huang, Qiuyuan, Wake, Naoki, Gong, Ran, Park, Jae Sung, Sarkar, Bidipta, Taori, Rohan, Noda, Yusuke, Terzopoulos, Demetri, Choi, Yejin, Ikeuchi, Katsushi, Vo, Hoi, Fei-Fei, Li, Gao, Jianfeng
Multi-modal AI systems will likely become a ubiquitous presence in our everyday lives. A promising approach to making these systems more interactive is to embody them as agents within physical and virtual environments. At present, systems leverage ex
Externí odkaz:
http://arxiv.org/abs/2401.03568
Conventions are crucial for strong performance in cooperative multi-agent games, because they allow players to coordinate on a shared strategy without explicit communication. Unfortunately, standard multi-agent reinforcement learning techniques, such
Externí odkaz:
http://arxiv.org/abs/2310.15414
Autor:
Gao, Jensen, Sarkar, Bidipta, Xia, Fei, Xiao, Ted, Wu, Jiajun, Ichter, Brian, Majumdar, Anirudha, Sadigh, Dorsa
Recent advances in vision-language models (VLMs) have led to improved performance on tasks such as visual question answering and image captioning. Consequently, these models are now well-positioned to reason about the physical world, particularly wit
Externí odkaz:
http://arxiv.org/abs/2309.02561
We present PantheonRL, a multiagent reinforcement learning software package for dynamic training interactions such as round-robin, adaptive, and ad-hoc training. Our package is designed around flexible agent objects that can be easily configured to s
Externí odkaz:
http://arxiv.org/abs/2112.07013
We propose a novel actor-critic, model-free reinforcement learning algorithm which employs a Bayesian method of parameter space exploration to solve environments. A Gaussian process is used to learn the expected return of a policy given the policy's
Externí odkaz:
http://arxiv.org/abs/2003.01074
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