research-article

Simultaneously Learning and Advising in Multiagent Reinforcement Learning

Authors:

Felipe Leno da Silva,

Anna Helena Reali CostaAuthors Info & Claims

AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

Pages 1100 - 1108

Published: 08 May 2017 Publication History

Abstract

Reinforcement Learning has long been employed to solve sequential decision-making problems with minimal input data. However, the classical approach requires a large number of interactions with an environment to learn a suitable policy. This problem is further intensified when multiple autonomous agents are simultaneously learning in the same environment. The teacher-student approach aims at alleviating this problem by integrating an advising procedure in the learning process, in which an experienced agent (human or not) can advise a student to guide her exploration. Even though previous works reported that an agent can learn faster when receiving advice, their proposals require that the teacher is an expert in the learning task. Sharing successful episodes can also accelerate learning, but this procedure requires a lot of communication between agents, which is unfeasible for domains in which communication is limited. Thus, we here propose a multiagent advising framework where multiple agents can advise each other while learning in a shared environment. If in any state an agent is unsure about what to do, it can ask for advice to other agents and may receive answers from agents that have more confidence in their actuation for that state. We perform experiments in a simulated Robot Soccer environment and show that the learning process is improved by incorporating this kind of advice.

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Cited By

Jin YWei SYuan JZhang XPelachaud CTaylor MFaliszewski PMascardi V(2022)Learning to Advise and Learning from Advice in Cooperative Multiagent Reinforcement LearningProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3536063(1645-1647)Online publication date: 9-May-2022
https://dl.acm.org/doi/10.5555/3535850.3536063
Zhu CLeung HHu SCai Y(2021)A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget ConstraintACM Transactions on Autonomous and Adaptive Systems10.1145/344726815:2(1-28)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3447268
Zhu CCai YLeung HHu SEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Learning by Reusing Previous Advice in Teacher-Student ParadigmProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398953(1674-1682)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398953
Show More Cited By

Index Terms

Simultaneously Learning and Advising in Multiagent Reinforcement Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination
      2. Multi-agent systems
  2. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning
      2. Reinforcement learning
        Multi-agent reinforcement learning

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Information & Contributors

Information

Published In

cover image ACM Other conferences

AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

May 2017

1914 pages

General Chairs:
Kate Larson
University of Waterloo, Canada
,
Michael Winikoff
University of Otago, New Zealand
,
Program Chairs:
Sanmay Das
Washington University in St. Louis, USA
,
Edmund Durfee
University of Michigan, USA

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IFAAMAS

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ACM: Association for Computing Machinery

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 08 May 2017

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CNPq
CAPES
Google Latin America Research Award
São Paulo Research Foundation (FAPESP)

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AAMAS '17 Paper Acceptance Rate 127 of 457 submissions, 28%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

Jin YWei SYuan JZhang XPelachaud CTaylor MFaliszewski PMascardi V(2022)Learning to Advise and Learning from Advice in Cooperative Multiagent Reinforcement LearningProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3536063(1645-1647)Online publication date: 9-May-2022
https://dl.acm.org/doi/10.5555/3535850.3536063
Zhu CLeung HHu SCai Y(2021)A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget ConstraintACM Transactions on Autonomous and Adaptive Systems10.1145/344726815:2(1-28)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3447268
Zhu CCai YLeung HHu SEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Learning by Reusing Previous Advice in Teacher-Student ParadigmProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398953(1674-1682)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398953
Kim DLiu MOmidshafiei SLopez-Cot SRiemer MHabibi GTesauro GMourad SCampbell MHow JEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Learning Hierarchical Teaching Policies for Cooperative AgentsProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398836(620-628)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398836
Silva FElkind EVeloso MAgmon NTaylor M(2019)Integrating Agent Advice and Previous Task Solutions in Multiagent Reinforcement LearningProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3332142(2447-2448)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3332142
Zhu CLeung HHu SCai YElkind EVeloso MAgmon NTaylor M(2019)A Q-values Sharing Framework for Multiple Independent Q-learnersProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3332099(2324-2326)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3332099
Da Silva FCosta A(2019)A survey on transfer learning for multiagent reinforcement learning systemsJournal of Artificial Intelligence Research10.1613/jair.1.1139664:1(645-703)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1613/jair.1.11396
Da Silva FTaylor MCosta A(2018)Autonomously reusing knowledge in multiagent reinforcement learningProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304788(5487-5493)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304652.3304788
Silva FCosta AAndre EKoenig SDastani MSukthankar G(2018)Object-Oriented Curriculum Generation for Reinforcement LearningProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237850(1026-1034)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3237850
Wang YLu WHao JWei JLeung HAndre EKoenig SDastani MSukthankar G(2018)Efficient Convention Emergence through Decoupled Reinforcement Social Learning with Teacher-Student MechanismProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237501(795-803)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3237501
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