413 citations · 1.4k across the 26 of their papers we have counts for
4 papers · 1 filter
Offline Reinforcement Learning for Visual Navigation
Dhruv Shah, Arjun Bhorkar, Hrish Leen +3
Reinforcement learning can enable robots to navigate to distant goals while optimizing user-specified reward functions, including preferences for following lanes, staying on paved…
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning
Laura Smith, Ilya Kostrikov, Sergey Levine
Deep reinforcement learning is a promising approach to learning policies in uncontrolled environments that do not require domain knowledge. Unfortunately, due to sample inefficienc…
Offline RL for Natural Language Generation with Implicit Language Q Learning
Charlie Snell, Ilya Kostrikov, Yi Su +2
Large language models distill broad knowledge from text corpora. However, they can be inconsistent when it comes to completing user specified tasks. This issue can be addressed by…
In Defense of the Unitary Scalarization for Deep Multi-Task Learning
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov +2
Recent multi-task learning research argues against unitary scalarization, where training simply minimizes the sum of the task losses. Several ad-hoc multi-task optimization algorit…