The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
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
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Evolutionary Computing in Multi-agent Environments: Operators
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Understanding cooperative co-evolutionary dynamics via simple fitness landscapes
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On identifying global optima in cooperative coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Improving coevolutionary search for optimal multiagent behaviors
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lenient learners in cooperative multiagent systems
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
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In concurrent cooperative multiagent learning, each agent simultaneously learns to improve the overall performance of the team, with no direct control over the actions chosen by its teammates. An agent's action selection directly influences the rewards received by all the agents, resulting in a co-adaptation among the concurrent learning processes. Co-adaptation can drive the team towards suboptimal solutions because agents tend to select those actions that are rewarded better, without any consideration for how such actions may affect the search of their teammates. We argue that to counter this tendency, agents should also prefer actions that inform their teammates about the structure of the joint search space in order to help them choose from among various action options. We analyze this approach in a cooperative coevolutionary framework, and we propose a new algorithm, iCCEA, that highlights the advantages of selecting informative actions. We show that iCCEA generally outperforms other cooperative coevolution algorithms on our test problems.