25 citations · 66 across the 7 of their papers we have counts for
7 papers
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
Rafael Rafailov, Kyle Hatch, Anikait Singh +9
Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large…
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner +2
Effective offline RL methods require properly handling out-of-distribution actions. Implicit Q-learning (IQL) addresses this by training a Q-function using only dataset actions thr…
Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Qiyang Li, Aviral Kumar, Ilya Kostrikov +1
Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While…
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
Kyle Stachowicz, Dhruv Shah, Arjun Bhorkar +2
We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL). Our system, FastRLAP (faster lap…
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…
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…