RoboCup Special Interest Group (SIG) on Multiagent Learning

SIG on Multiagent Learning

The primary aim of this SIG is to use the RoboCup domain for focussed investigations of algorithms and techniques for collaborative and adversarial learning. It builds upon the IJCAI-97 synthetic agents learning challenge by expanding it to include multiagent learning challenges in all the different RoboCup leagues and by bringing together researchers with this common interest in rapid, focussed discussions.

Specific goals include:

In the simulator, the SIG will aim to support a modified version of the simulator and publicly available clients to implement such subtasks. Milestones could then be concretely specified in terms of offensive or defensive learning-based performance against a fixed opponent.

A specific challenge in the real-robot leagues is to create and test hardware-independent learning algorithms that could be used by more than one team. The SIG will encourage hardware-based teams to pair up with other hardware or software-based teams to investigate common learning algorithms on their different platforms.

Organizing committee members:

General Goals:

RoboCup SIGs are being formed with the following goals:

UPDATED Learning Approaches

List of machine learning approaches to RoboCup.


Learning Benchmarks


Here you can find the proposed benchmarks for the Multiagent Learning Task.

Mailing List:

If you're interested, please join the mailing list.

Other RoboCup SIGs:

The complete list of RoboCup SIGs and instructions for how to propose a new RoboCup SIG are available from the main RoboCup SIG website.

Peter Stone
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