The ability of an AI agent to assist other agents, such as humans, is an important and challenging goal, which requires the assisting agent to reason about the behavior and infer the goals of the assisted agent. Training such an ability by using reinforcement learning usually requires large amounts of online training, which is difficult and costly. On the other hand, offline data about the behavior of the assisted agent might be available, but is non-trivial to take advantage of by methods such as offline reinforcement learning. We introduce methods where the capability to create a representation of the behavior is first pre-trained with offline data, after which only a small amount of interaction data is needed to learn an assisting policy. We test the setting in a gridworld where the helper agent has the capability to manipulate the environment of the assisted artificial agents, and introduce three different scenarios where the assistance considerably improves the performance of the assisted agents.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
Number of pages12
ISBN (Print)978-3-030-86379-1
Publication statusPublished - 2021
MoE publication typeA4 Conference publication
EventInternational Conference on Artificial Neural Networks - Virtual, Online
Duration: 14 Sept 202117 Sept 2021
Conference number: 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12894 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Artificial Neural Networks
Abbreviated titleICANN
CityVirtual, Online


  • Cooperative AI
  • Deep reinforcement learning
  • Helper agent
  • Meta-learning
  • Modelling other agents


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