38 citations · 122 across the 6 of their papers we have counts for
7 papers · 1 filter
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Avi Singh, Huihan Liu, Gaoyue Zhou +3
Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn.…
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Avi Singh, Albert Yu, Jonathan Yang +3
Reinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task. Since the amou…
The Ingredients of Real-World Robotic Reinforcement Learning
Henry Zhu, Justin Yu, Abhishek Gupta +5
The success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversig…
End-to-End Robotic Reinforcement Learning without Reward Engineering
Avi Singh, Larry Yang, Kristian Hartikainen +2
The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory i…
Few-Shot Goal Inference for Visuomotor Learning and Planning
Annie Xie, Avi Singh, Sergey Levine +1
Reinforcement learning and planning methods require an objective or reward function that encodes the desired behavior. Yet, in practice, there is a wide range of scenarios where an…
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition
Justin Fu, Avi Singh, Dibya Ghosh +2
The design of a reward function often poses a major practical challenge to real-world applications of reinforcement learning. Approaches such as inverse reinforcement learning atte…