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
Neuro-Dynamic Programming
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Context-specific multiagent coordination and planning with factored MDPs
Eighteenth national conference on Artificial intelligence
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Reasoning about joint beliefs for execution-time communication decisions
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Hierarchical multi-agent reinforcement learning
Autonomous Agents and Multi-Agent Systems
Exploiting factored representations for decentralized execution in multiagent teams
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Interaction-driven Markov games for decentralized multiagent planning under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Value-function reinforcement learning in Markov games
Cognitive Systems Research
Learning multi-agent state space representations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Coordinated learning in multiagent MDPs with infinite state-space
Autonomous Agents and Multi-Agent Systems
Multi-policy optimization in self-organizing systems
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
Generalized learning automata for multi-agent reinforcement learning
AI Communications - European Workshop on Multi-Agent Systems (EUMAS) 2009
Solving delayed coordination problems in MAS
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Reinforcement Learning of Communication in a Multi-agent Context
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Autonomic multi-policy optimization in pervasive systems: Overview and evaluation
ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
Solving sparse delayed coordination problems in multi-agent reinforcement learning
ALA'11 Proceedings of the 11th international conference on Adaptive and Learning Agents
Holonification of a network of agents based on graph theory
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
Using conflict resolution to inform decentralized learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limited to specific parts of the state space, as previous work has successfully shown. In this work, we assume that such needs are unknown and we investigate coordination learning in multiagent settings. We contribute a reinforcement learning based algorithm in which independent decision-makers/agents learn both individual policies and when and how to coordinate. We focus on problems in which the interaction between the agents is sparse, exploiting this property to minimize the coupling of the learning processes for the different agents. We introduce a two-layer extension of Q-learning, in which we augment the action space of each agent with a coordination action that uses information from other agents to decide the correct action. Our results show that our agents learn both to act coordinate and to act independently, in the different regions of the space where they need to, and need not to, coordinate, respectively.