Difference between revisions of "Multi-Agent Learning (MAL) Task"

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=== 2010 ===
 
=== 2010 ===
* ([[Shoham & Powers, 2010]]) ⇒ Yoav Shoham, and Rob Powers. ([[2010]]). [http://robotics.stanford.edu/~shoham/www%20papers/ML%20encyc%20MAL2.pdf "Multi-Agent Learning I: Problem Definition"].  
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* ([[Shoham & Powers, 2010]]) ⇒ [[Yoav Shoham]], and Rob Powers. ([[2010]]). [http://robotics.stanford.edu/~shoham/www%20papers/ML%20encyc%20MAL2.pdf "Multi-Agent Learning I: Problem Definition"].  
 
** QUOTE: [[Multi-agent learning]] refers to settings in which [[multiple agent]]s learn simultaneously. Usually defined in a [[game theoretic setting]], specifically in [[repeated game]]s or [[stochastic game]]s, the key feature that distinguishes [[multi-agent learning]] from [[single-agent learning]] is that in the former the [[learning]] of one [[agent]] impacts he learning of others. As a result neither the problem definition for [[mutli-agent learning]], nor the [[algorithm]]s offered, follow in a straightforward way from the single-agent case. In this second of two entries on the subject we focus on algorithms.
 
** QUOTE: [[Multi-agent learning]] refers to settings in which [[multiple agent]]s learn simultaneously. Usually defined in a [[game theoretic setting]], specifically in [[repeated game]]s or [[stochastic game]]s, the key feature that distinguishes [[multi-agent learning]] from [[single-agent learning]] is that in the former the [[learning]] of one [[agent]] impacts he learning of others. As a result neither the problem definition for [[mutli-agent learning]], nor the [[algorithm]]s offered, follow in a straightforward way from the single-agent case. In this second of two entries on the subject we focus on algorithms.
  

Revision as of 14:40, 13 August 2019

A Multi-Agent Learning (MAL) Task is an agent learning task that is a joint learning task.



References

2017a

2017b

2010

2002

  • http://www.cs.rutgers.edu/~mlittman/topics/nips02/
    • QUOTE: More and more, machine learning is being explored as a vital component to address challenges in multi-agent systems. For example, many application domains are envisioned in which teams of software agents or robots learn to cooperate amongst each other and with human beings to achieve global objectives. Learning may also be essential in many non-cooperative domains such as economics and finance, where classical game-theoretic solutions are either infeasible or inappropriate.

      At the same time, multi-agent learning poses significant theoretical challenges, particularly in understanding how agents can learn and adapt in the presence of other agents that are simultaneously learning and adapting. This is a fertile area of research that seems ripe for progress: the numerous and significant theoretical developments of the 1990s, in fields such as Bayesian, game-theoretic, decision-theoretic, and evolutionary learning, can now be extended to more challenging multi-agent scenarios.

      This workshop on theory and practice in multi-agent learning is intended to be broad in scope and informal in style.