Technical Note: \cal Q-Learning
Machine Learning
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
Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Best-Response Multiagent Learning in Non-Stationary Environments
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Reinforcement Learning for Stochastic Cooperative Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multiagent learning in the presence of agents with limitations
Multiagent learning in the presence of agents with limitations
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
Learning to cooperate in multi-agent social dilemmas
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Perspectives on multiagent learning
Artificial Intelligence
The possible and the impossible in multi-agent learning
Artificial Intelligence
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
A machine-learning approach to multi-robot coordination
Engineering Applications of Artificial Intelligence
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
Predicting and preventing coordination problems in cooperative Q-learning systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Convergence of independent adaptive learners
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Multiagent reinforcement learning for a planetary exploration multirobot system
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Value-function reinforcement learning in Markov games
Cognitive Systems Research
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In multi-agent systems, the presence of learning agents can cause the environment to be non-Markovian from an agent's perspective thus violating the property that traditional single-agent learning methods rely upon. This paper formalizes some known intuition about concurrently learning agents by providing formal conditions that make the environment non-Markovian from an independent (non-communicative) learner's perspective. New concepts are introduced like the divergent learning paths and the observability of the effects of others' actions. To illustrate the formal concepts, a case study is also presented. These findings are significant because they both help to understand failures and successes of existing learning algorithms as well as being suggestive for future work.