90 citations · 185 across the 6 of their papers we have counts for
4 papers · 1 filter
Meta-Learning Requires Meta-Augmentation
Janarthanan Rajendran, Alex Irpan, Eric Jang
Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that quickly updates that model when given examples from a new ta…
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
Ted Xiao, Eric Jang, Dmitry Kalashnikov +4
We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robo…
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Allan Zhou, Eric Jang, Daniel Kappler +7
Imitation learning allows agents to learn complex behaviors from demonstrations. However, learning a complex vision-based task may require an impractical number of demonstrations.…
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor +8
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of g…