The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Nash Convergence of Gradient Dynamics in General-Sum Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Adaptive policy gradient in multiagent learning
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Multiagent reinforcement learning and self-organization in a network of agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Reinforcement learning with utility-aware agents for market-based resource allocation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Non-linear dynamics in multiagent reinforcement learning algorithms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Efficient multi-agent reinforcement learning through automated supervision
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Meta-level Control of Multiagent Learning in Dynamic Repeated Resource Sharing Problems
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Learning the IPA market with individual and social rewards
Web Intelligence and Agent Systems
Integrating organizational control into multi-agent learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A multiagent reinforcement learning algorithm with non-linear dynamics
Journal of Artificial Intelligence Research
Globally Optimal Multi-agent Reinforcement Learning Parameters in Distributed Task Assignment
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Using graph analysis to study networks of adaptive agent
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Self-organization for coordinating decentralized reinforcement learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A novel multi-agent reinforcement learning approach for job scheduling in Grid computing
Future Generation Computer Systems
Cognitive policy learner: biasing winning or losing strategies
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Recursive adaptation of stepsize parameter for non-stationary environments
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Multiagent meta-level control for radar coordination
Web Intelligence and Agent Systems
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The distributed task allocation problem occurs in domains like web services, the grid, and other distributed systems. In this problem, the system consists of servers and mediators. Servers execute tasks and may differ in their capabilities, e.g. one server may take more time than the other in executing the same task. Mediators act on behalf of users, which can potentially be other mediators, and are responsible for receiving tasks from users and allocating them to servers.This work introduces a new gradient ascent learning algorithm that outperforms state of the art multiagent learners on this problem. We experimentally show that our algorithm converges faster and is less sensitive to tuning parameters than other algorithms. We also provide an informal proof that WPL has the same convergence guarantee as the best known algorithm, GIGA-WoLF. We also show that our algorithm converges in Jordan's and Shapley's games where many other algorithms fail to converge. Finally, we verify the practicality of our algorithm in the distributed task allocation domain, comparing its performance to an optimal global solution.