no code implementations • 31 Jan 2024 • Jiezhi Yang, Khushi Desai, Charles Packer, Harshil Bhatia, Nicholas Rhinehart, Rowan Mcallister, Joseph Gonzalez
We demonstrate the utility of our method in realistic scenarios using the CARLA driving simulator, where CARFF can be used to enable efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving visual occlusions.
1 code implementation • 12 Oct 2023 • Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G. Patil, Ion Stoica, Joseph E. Gonzalez
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis.
no code implementations • NeurIPS 2021 • Charles Packer, Pieter Abbeel, Joseph E. Gonzalez
Meta-reinforcement learning (meta-RL) has proven to be a successful framework for leveraging experience from prior tasks to rapidly learn new related tasks, however, current meta-RL approaches struggle to learn in sparse reward environments.
1 code implementation • 21 Apr 2021 • Nicholas Rhinehart, Jeff He, Charles Packer, Matthew A. Wright, Rowan Mcallister, Joseph E. Gonzalez, Sergey Levine
Humans have a remarkable ability to make decisions by accurately reasoning about future events, including the future behaviors and states of mind of other agents.
1 code implementation • ICLR 2019 • Charles Packer, Katelyn Gao, Jernej Kos, Philipp Krähenbühl, Vladlen Koltun, Dawn Song
Our aim is to catalyze community-wide progress on generalization in deep RL.
Out-of-Distribution Generalization reinforcement-learning +1
no code implementations • 26 Jun 2018 • Charles Packer, Julian McAuley, Arnau Ramisa
Visually-aware recommender systems use visual signals present in the underlying data to model the visual characteristics of items and users' preferences towards them.
1 code implementation • 31 Mar 2016 • Ruining He, Charles Packer, Julian McAuley
Identifying relationships between items is a key task of an online recommender system, in order to help users discover items that are functionally complementary or visually compatible.