565 citations · 4.4k across the 119 of their papers we have counts for
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Data-Driven Offline Decision-Making via Invariant Representation Learning
Han Qi, Yi Su, Aviral Kumar +1
The goal in offline data-driven decision-making is synthesize decisions that optimize a black-box utility function, using a previously-collected static dataset, with no active inte…
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints
Anikait Singh, Aviral Kumar, Quan Vuong +2
Offline reinforcement learning (RL) learns policies entirely from static datasets, thereby avoiding the challenges associated with online data collection. Practical applications of…
Dual Generator Offline Reinforcement Learning
Quan Vuong, Aviral Kumar, Sergey Levine +1
In offline RL, constraining the learned policy to remain close to the data is essential to prevent the policy from outputting out-of-distribution (OOD) actions with erroneously ove…
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai +2
Reinforcement learning provides an automated framework for learning behaviors from high-level reward specifications, but in practice the choice of reward function can be crucial fo…
You Only Live Once: Single-Life Reinforcement Learning
Annie S. Chen, Archit Sharma, Sergey Levine +1
Reinforcement learning algorithms are typically designed to learn a performant policy that can repeatedly and autonomously complete a task, usually starting from scratch. However,…
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
Philippe Hansen-Estruch, Amy Zhang, Ashvin Nair +2
Building generalizable goal-conditioned agents from rich observations is a key to reinforcement learning (RL) solving real world problems. Traditionally in goal-conditioned RL, an…