3 code implementations • 16 Apr 2019 • Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine
In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task.
no code implementations • NeurIPS 2018 • Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine
We propose variational inverse control with events (VICE), which generalizes inverse reinforcement learning methods to cases where full demonstrations are not needed, such as when only samples of desired goal states are available.
no code implementations • ICCV 2017 • Avi Singh, Larry Yang, Sergey Levine
We show that pairing interaction data from just a single environment with a diverse dataset of weakly labeled data results in greatly improved generalization to unseen environments, and show that this generalization depends on both the auxiliary objective and the attentional architecture that we propose.