307 citations · 724 across the 15 of their papers we have counts for
4 papers · 1 filter
Offline Reinforcement Learning with Implicit Q-Learning
Ilya Kostrikov, Ashvin Nair, Sergey Levine
Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same tim…
Offline Meta-Reinforcement Learning with Online Self-Supervision
Vitchyr H. Pong, Ashvin Nair, Laura Smith +2
Meta-reinforcement learning (RL) methods can meta-train policies that adapt to new tasks with orders of magnitude less data than standard RL, but meta-training itself is costly and…
What Can I Do Here? Learning New Skills by Imagining Visual Affordances
Alexander Khazatsky, Ashvin Nair, Daniel Jing +1
A generalist robot equipped with learned skills must be able to perform many tasks in many different environments. However, zero-shot generalization to new settings is not always p…
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies
Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair +3
Can we use reinforcement learning to learn general-purpose policies that can perform a wide range of different tasks, resulting in flexible and reusable skills? Contextual policies…