no code implementations • 14 Dec 2023 • Alireza Ghaffari, Justin Yu, Mahsa Ghazvini Nejad, Masoud Asgharian, Boxing Chen, Vahid Partovi Nia
The benefit of using integers for outlier values is that it enables us to use operator tiling to avoid performing 16-bit integer matrix multiplication to address this problem effectively.
no code implementations • 15 Jul 2021 • Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr Pong, Aurick Zhou, Justin Yu, Sergey Levine
In this work, we show that an uncertainty aware classifier can solve challenging reinforcement learning problems by both encouraging exploration and provided directed guidance towards positive outcomes.
no code implementations • 22 Apr 2021 • Abhishek Gupta, Justin Yu, Tony Z. Zhao, Vikash Kumar, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, Sergey Levine
This work shows the ability to learn dexterous manipulation behaviors in the real world with RL without any human intervention.
no code implementations • 1 Jan 2021 • Kevin Li, Abhishek Gupta, Vitchyr H. Pong, Ashwin Reddy, Aurick Zhou, Justin Yu, Sergey Levine
In this work, we study a more tractable class of reinforcement learning problems defined by data that provides examples of successful outcome states.
no code implementations • ICLR 2020 • Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine
The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.
no code implementations • 27 Apr 2020 • Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, Sergey Levine
In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world.