[2301.08028] A Survey of Meta-Reinforcement Learning
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status Get status notifications via email or slack
[2301.08028] A Tutorial on Meta-Reinforcement Learning Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Machine Learning arXiv:2301.08028 (cs) [Submitted on 19 Jan 2023 ( v1 ), last revised 29 May 2025 (this version, v4)] Title: A Tutorial on Meta-Reinforcement Learning Authors: Jacob Beck , Risto Vuorio , Evan Zheran Liu , Zheng Xiong , Luisa Zintgraf , Chelsea Finn , Shimon Whiteson View a PDF of the paper titled A Tutorial on Meta-Reinforcement Learning, by Jacob Beck and 6 other
Explore this link on the map →related reading
- Key Papers in Deep RL - Spinning Up documentationspinningup.openai.com
- Just Ask for Generalization | Eric Jangevjang.com
- Evolution as Backstop for Reinforcement Learning · Gwern.netgwern.net
- Optimizing LLM Test-Time Compute Involves Solving a Meta-RL Problem – Machine Learning Blog | ML@CMU | Carnegie Mellon Universityblog.ml.cmu.edu
- [1703.03400] Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networksarxiv.org
- Metalearning or Learning to Learn Since 1987people.idsia.ch
- Selected Publicationsjoschu.net
- Bootstrapped Meta-Learningarxiv.org
- Meta Learninglilianweng.github.io
- DataRater: Meta-Learned Dataset Curationarxiv.org
- Deep Reinforcement Learning Doesn't Work Yetalexirpan.com
- Reinforcement learning - Wikipediaen.wikipedia.org