Multi-agent reinforcement learning: independent vs. cooperative agents
Readings in agents
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
Evolving neural networks through augmenting topologies
Evolutionary Computation
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Implicit Imitation in Multiagent Reinforcement Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
A Game-Theoretic Approach to the Simple Coevolutionary Algorithm
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A Taxonomy for artificial embryogeny
Artificial Life
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Picbreeder: evolving pictures collaboratively online
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
The sensitivity of HyperNEAT to different geometric representations of a problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A case study on the critical role of geometric regularity in machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Improving coevolutionary search for optimal multiagent behaviors
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Transfer learning through indirect encoding
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Neuroevolution of mobile ad hoc networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Constraining connectivity to encourage modularity in HyperNEAT
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Multirobot behavior synchronization through direct neural network communication
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
Command and control of teams of autonomous systems
Proceedings of the 17th Monterey conference on Large-Scale Complex IT Systems: development, operation and management
Confronting the challenge of learning a flexible neural controller for a diversity of morphologies
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Evolving multimodal controllers with HyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Critical factors in the performance of hyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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A major challenge for traditional approaches to multiagent learning is to train teams that easily scale to include additional agents. The problem is that such approaches typically encode each agent's policy separately. Such separation means that computational complexity explodes as the number of agents in the team increases, and also leads to the problem of reinvention: Skills that should be shared among agents must be rediscovered separately for each agent. To address this problem, this paper presents an alternative evolutionary approach to multiagent learning called multiagent HyperNEAT that encodes the team as a pattern of related policies rather than as a set of individual agents. To capture this pattern, a policy geometry is introduced to describe the relationship between each agent's policy and its canonical geometric position within the team. Because policy geometry can encode variations of a shared skill across all of the policies it represents, the problem of reinvention is avoided. Furthermore, because the policy geometry of a particular team can be sampled at any resolution, it acts as a heuristic for generating policies for teams of any size, producing a powerful new capability for multiagent learning. In this paper, multiagent HyperNEAT is tested in predator-prey and room-clearing domains. In both domains the results are effective teams that can be successfully scaled to larger team sizes without any further training.