524 citations · 2.7k across the 55 of their papers we have counts for
6 papers · 1 filter
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
Abhishek Gupta, Vikash Kumar, Corey Lynch +2
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable,…
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He +7
Meta-reinforcement learning algorithms can enable robots to acquire new skills much more quickly, by leveraging prior experience to learn how to learn. However, much of the current…
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
Cristian Bodnar, Adrian Li, Karol Hausman +2
The distributional perspective on reinforcement learning (RL) has given rise to a series of successful Q-learning algorithms, resulting in state-of-the-art performance in arcade ga…
Training an Interactive Helper
Mark Woodward, Chelsea Finn, Karol Hausman
Developing agents that can quickly adapt their behavior to new tasks remains a challenge. Meta-learning has been applied to this problem, but previous methods require either specif…
Dynamics-Aware Unsupervised Discovery of Skills
Archit Sharma, Shixiang Gu, Sergey Levine +2
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms…
Learning to Interactively Learn and Assist
Mark Woodward, Chelsea Finn, Karol Hausman
When deploying autonomous agents in the real world, we need effective ways of communicating objectives to them. Traditional skill learning has revolved around reinforcement and imi…