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ROBOCUP
2009
Springer

Learning Complementary Multiagent Behaviors: A Case Study

13 years 10 months ago
Learning Complementary Multiagent Behaviors: A Case Study
As the reach of multiagent reinforcement learning extends to more and more complex tasks, it is likely that the diverse challenges posed by some of these tasks can only be addressed by combining the strengths of different learning methods. While this important aspect of learning is yet to receive theoretical analysis, useful insights can be gained from its applications to concrete tasks. This paper presents one such case study grounded in the robot soccer context. The task we consider is Keepaway, a popular benchmark for multiagent reinforcement learning. Whereas previous successful results in this domain have limited learning to an isolated, infrequent decision that amounts to a turn-taking behavior (passing), we expand the agents’ learning capability to also include a much more ubiquitous action (moving without the ball, or getting open), such that at any given time, multiple agents are executing learned behaviors simultaneously. We introduce a policy search method for learning â...
Shivaram Kalyanakrishnan, Peter Stone
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where ROBOCUP
Authors Shivaram Kalyanakrishnan, Peter Stone
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