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 • 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.
2 code implementations • ICLR 2021 • Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Devin, Benjamin Eysenbach, Sergey Levine
Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.
Multi-Goal Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 25 Sep 2019 • Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devin, Benjamin Eysenbach, Sergey Levine
By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic complexities and optimization challenges of reinforcement learning, at the cost of requiring an expert demonstrator -- typically a person -- to provide the demonstrations.