2109.10813.pdf
arxiv.org · 7,071 words · saved by 1 readers
N/A
A Workflow for Offline Model-Free Robotic Reinforcement Learning Aviral Kumar?,1 , Anikait Singh?,1 , Stephen Tian1 , Chelsea Finn2 , Sergey Levine1 1 UC Berkeley, 2 Stanford University (∗ Equal Contribution) aviralk@berkeley.edu, asap7772@berkeley.edu arXiv:2109.10813v2 [cs.LG] 23 Sep 2021…
saved by
related reading
- 2301.00993v1.pdfarxiv.org
- [2103.06326] S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learningarxiv.org
- Exploration for the Efficient Deployment of Reinforcement Learning Agentsopenreview.net
- Should I Use Offline RL or Imitation Learning? – The Berkeley Artificial Intelligence Research Blogbair.berkeley.edu
- [2109.01115] Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotationarxiv.org
- [1709.06560] Deep Reinforcement Learning that Mattersarxiv.org
- Reinforcement Learning in Newcomblike Problemsproceedings.neurips.cc
- [2005.01643] Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemsar5iv.labs.arxiv.org
- [2204.05618] When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?ar5iv.labs.arxiv.org
- [2602.19362] LLMs Can Learn to Reason Via Off-Policy RLarxiv.org
- 2106.01345arxiv.org
- [2602.05999] On the Role of Computation in Reinforcement Learningarxiv.org