Technical Note: \cal Q-Learning
Machine Learning
A Graph-based Ant system and its convergence
Future Generation Computer Systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Modeling the dynamics of ant colony optimization
Evolutionary Computation
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
Distributed path planning for mobile robots using a swarm of interacting reinforcement learners
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Switching dynamics of multi-agent learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
State-coupled replicator dynamics
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Swarm Intelligence: Ant-Based Robot Path Planning
IAS '09 Proceedings of the 2009 Fifth International Conference on Information Assurance and Security - Volume 01
Frequency adjusted multi-agent Q-learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Networks of learning automata and limiting games
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
IEEE Computational Intelligence Magazine
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Swarm intelligence has been successfully applied in various domains, e.g., path planning, resource allocation and data mining. Despite its wide use, a theoretical framework in which the behavior of swarm intelligence can be formally understood is still lacking. This article starts by formally deriving the evolutionary dynamics of ant colony optimization, an important swarm intelligence algorithm. We then continue to formally link these to reinforcement learning. Specifically, we show that the attained evolutionary dynamics are equivalent to the dynamics of Q-learning. Both algorithms are equivalent to a dynamical system known as the replicator dynamics in the domain of evolutionary game theory. In conclusion, the process of improvement described by the replicator dynamics appears to be a fundamental principle which drives processes in swarm intelligence, evolution, and learning.