Learning automata: an introduction
Learning automata: an introduction
Technical Note: \cal Q-Learning
Machine Learning
Asynchronous Stochastic Approximation and Q-Learning
Machine Learning
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
General principles of learning-based multi-agent systems
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Using collective intelligence to route Internet traffic
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Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
On No-Regret Learning, Fictitious Play, and Nash Equilibrium
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Collective Intelligence and Braess' Paradox
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Dispersion games: general definitions and some specific learning results
Eighteenth national conference on Artificial intelligence
Transition-independent decentralized markov decision processes
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Performance models for large scale multiagent systems: using distributed POMDP building blocks
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Adaptive policy gradient in multiagent learning
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A selection-mutation model for q-learning in multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Multi-agent learning in extensive games with complete information
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Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Scientific Programming - Distributed Computing and Applications
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An evolutionary game-theoretic comparison of two double-auction market designs
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
Theoretical advantages of lenient Q-learners: an evolutionary game theoretic perspective
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Effective tag mechanisms for evolving coordination
Proceedings of the 6th 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
Switching dynamics of multi-agent learning
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Multi-agent Learning Dynamics: A Survey
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Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
An evolutionary model of multi-agent learning with a varying exploration rate
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A multiagent reinforcement learning algorithm with non-linear dynamics
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Frequency adjusted multi-agent Q-learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
The Dynamics of Multi-Agent Reinforcement Learning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
An algorithmic game theory study of wholesale electricity markets based on central auction
Integrated Computer-Aided Engineering - Multi-Agent Systems for Energy Management
Multi-goal Q-learning of cooperative teams
Expert Systems with Applications: An International Journal
Evolutionary dynamics of regret minimization
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Bifurcation analysis of reinforcement learning agents in the Selten's horse game
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
The world of independent learners is not markovian
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Replicator dynamics for multi-agent learning: an orthogonal approach
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Decentralized learning in wireless sensor networks
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Evolutionary dynamics of ant colony optimization
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Continuous strategy replicator dynamics for multi-agent Q-learning
Autonomous Agents and Multi-Agent Systems
Multi-agent learning and the reinforcement gradient
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Adaptive learning algorithm of self-organizing teams
Expert Systems with Applications: An International Journal
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In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not stationary and the Markov property is not valid. This requires agents to be adaptive. RL is a natural approach to model the learning of individual agents. These Learning algorithms are however known to be sensitive to the correct choice of parameter settings for single agent systems. This issue is more prevalent in the MAS case due to the changing interactions amongst the agents. It is largely an open question for a developer of MAS of how to design the individual agents such that, through learning, the agents as a collective arrive at good solutions. We will show that modeling RL in MAS, by taking an evolutionary game theoretic point of view, is a new and potentially successful way to guide learning agents to the most suitable solution for their task at hand. We show how evolutionary dynamics (ED) from Evolutionary Game Theory can help the developer of a MAS in good choices of parameter settings of the used RL algorithms. The ED essentially predict the equilibriums outcomes of the MAS where the agents use individual RL algorithms. More specifically, we show how the ED predict the learning trajectories of Q-Learners for iterated games. Moreover, we apply our results to (an extension of) the COllective INtelligence framework (COIN). COIN is a proved engineering approach for learning of cooperative tasks in MASs. The utilities of the agents are re-engineered to contribute to the global utility. We show how the improved results for MAS RL in COIN, and a developed extension, are predicted by the ED.