公开(公告)号：US20210237266A1

公开(公告)日：2021-08-05

申请号：US17052679

申请日：2019-06-14

Applicant: Google LLC

Inventor： Dmitry Kalashnikov ,  Alexander Irpan ,  Peter Pastor Sampedro ,  Julian Ibarz ,  Alexander Herzog ,  Eric Jang ,  Deirdre Quillen ,  Ethan Holly ,  Sergey Levine

IPC: B25J9/16 ,  G06N3/08

Abstract: Using large-scale reinforcement learning to train a policy model that can be utilized by a robot in performing a robotic task in which the robot interacts with one or more environmental objects. In various implementations, off-policy deep reinforcement learning is used to train the policy model, and the off-policy deep reinforcement learning is based on self-supervised data collection. The policy model can be a neural network model. Implementations of the reinforcement learning utilized in training the neural network model utilize a continuous-action variant of Q-learning. Through techniques disclosed herein, implementations can learn policies that generalize effectively to previously unseen objects, previously unseen environments, etc.