25 citations · 95 across the 13 of their papers we have counts for
4 papers · 1 filter
Efficient Online Reinforcement Learning with Offline Data
Philip J. Ball, Laura Smith, Ilya Kostrikov +1
Sample efficiency and exploration remain major challenges in online reinforcement learning (RL). A powerful approach that can be applied to address these issues is the inclusion of…
B-Pref: Benchmarking Preference-Based Reinforcement Learning
Kimin Lee, Laura Smith, Anca Dragan +1
Reinforcement learning (RL) requires access to a reward function that incentivizes the right behavior, but these are notoriously hard to specify for complex tasks. Preference-based…
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…
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
Kimin Lee, Laura Smith, Pieter Abbeel
Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet eas…