all AI news
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning. (arXiv:2207.02249v1 [cs.MA])
July 7, 2022, 1:10 a.m. | Lukas Schäfer, Filippos Christianos, Amos Storkey, Stefano V. Albrecht
cs.LG updates on arXiv.org arxiv.org
Successful deployment of multi-agent reinforcement learning often requires
agents to adapt their behaviour. In this work, we discuss the problem of
teamwork adaptation in which a team of agents needs to adapt their policies to
solve novel tasks with limited fine-tuning. Motivated by the intuition that
agents need to be able to identify and distinguish tasks in order to adapt
their behaviour to the current task, we propose to learn multi-agent task
embeddings (MATE). These task embeddings are trained using …
arxiv learning reinforcement reinforcement learning teamwork
More from arxiv.org / cs.LG updates on arXiv.org
Regularization by Texts for Latent Diffusion Inverse Solvers
1 day, 5 hours ago |
arxiv.org
When can transformers reason with abstract symbols?
1 day, 5 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Scientist (m/f/x/d)
@ Symanto Research GmbH & Co. KG | Spain, Germany
Data Scientist 3
@ Wyetech | Annapolis Junction, Maryland
Technical Program Manager, Robotics
@ DeepMind | Mountain View, California, US
Machine Learning Engineer
@ Issuu | Braga
Business Intelligence Manager
@ Intuitive | Bengaluru, India
Expert Data Engineer (m/w/d)
@ REWE International Dienstleistungsgesellschaft m.b.H | Wien, Austria