Search Results for author: Larry Yang

Found 3 papers, 1 papers with code

End-to-End Robotic Reinforcement Learning without Reward Engineering

3 code implementations16 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.

reinforcement-learning Reinforcement Learning (RL)

Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition

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.

Continuous Control reinforcement-learning +1

GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images

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.

Binary Classification Domain Adaptation

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