References of "Bakker, Bram"
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See detailMultiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs
Kuyer, Lior; Whiteson, Shimon; Bakker, Bram et al

in Proceedings of 19th European Conference on Machine Learning (2008)

Since traffic jams are ubiquitous in the modern world, optimizing, the behavior of traffic lights for efficient traffic flow is a critically important goal. Though most current traffic lights use simple ... [more ▼]

Since traffic jams are ubiquitous in the modern world, optimizing, the behavior of traffic lights for efficient traffic flow is a critically important goal. Though most current traffic lights use simple heuristic protocols, more efficient controllers can be discovered automatically via multiagent reinforcement learning where each agent controls a single traffic light. However, in previous work on this approach, agents select only locally optimal actions without coordinating their behavior. This paper extends this approach to include explicit coordination between neighboring traffic lights. Coordination is achieved using the max-plus algorithm, which estimates the optimal joint action by sending locally optimized messages among connected agents. This paper presents the first application of max-plus to a large-scale problem and thus verifies its efficacy in realistic settings. It also provides empirical evidence that max-plus performs well on cyclic graphs, though it has been proven to converge only for tree-structured graphs. Furthermore, it provides a new understanding of the properties a traffic network must have for such coordination to be beneficial and shows that max-plus outperforms previous methods on networks that possess those properties. [less ▲]

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See detailUtile coordination: Learning interdependencies among cooperative agents
Kok, Jelle R.; Hoen, Eter Jan; Bakker, Bram et al

in EEE Symp. on Computational Intelligence and Games, Colchester, Essex (2005)

We describe Utile Coordination, an algorithm that allows a multiagent system to learn where and how to coordinate. The method starts with uncoordinated learners and maintains statistics on expected ... [more ▼]

We describe Utile Coordination, an algorithm that allows a multiagent system to learn where and how to coordinate. The method starts with uncoordinated learners and maintains statistics on expected returns. Coordination dependencies are dynamically added if the statistics indicate a statistically significant benefit. This results in a compact state representation because only necessary coordination is modeled. We apply our method within the framework of coordination graphs in which value rules represent the coordination dependencies between the agents for a specific context. The algorithm is first applied on a small illustrative problem, and next on a large predator-prey problem in which two predators have to capture a single prey. [less ▲]

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