Learning automata: an introduction
Learning automata: an introduction
Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
Multiagent learning is not the answer. It is the question
Artificial Intelligence
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
Theoretical advantages of lenient Q-learners: an evolutionary game theoretic perspective
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Learning automata as a basis for multi agent reinforcement learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning the global maximum with parameterized learning automata
IEEE Transactions on Neural Networks
Rational Bidding Using Reinforcement Learning
GECON '08 Proceedings of the 5th international workshop on Grid Economics and Business Models
Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
Towards requirement analysis pattern for learning agents
AOSE'10 Proceedings of the 11th international conference on Agent-oriented software engineering
Analysis patterns for learning agents
Proceedings of the 15th European Conference on Pattern Languages of Programs
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In this paper we compare state-of-the-art multi-agent reinforcement learning algorithms in a wide variety of games. We consider two types of algorithms: value iteration and policy iteration. Four characteristics are studied: initial conditions, parameter settings, convergence speed, and local versus global convergence. Global convergence is still difficult to achieve in practice, despite existing theoretical guarantees. Multiple visualizations are included to provide a comprehensive insight into the learning dynamics.