Distributed reinforcement learning for a traffic engineering application
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Communication decisions in multi-agent cooperation: model and experiments
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Graphical Models in Local, Asymmetric Multi-Agent Markov Decision Processes
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Cooperation in stochastic games through communication
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Multiple stochastic learning automata for vehicle path control in an automated highway system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Real world multiagent coordination problems are important issues for reinforcement learning techniques. In general, these problems are partially observable and this characteristic makes the solution computation intractable. Most of the existing approaches calculate exact or approximate solutions using the world model for only one agent. To handle a special case of partial observability, this article presents an approach to approximate the policy measuring a degree of observability for pure cooperative vehicle coordination problem. We compare empirically the performance of the learned policy for totally observable problems and performances of policies for different degrees of observability. If each degree of observability is associated with communication costs, multiagent system designers are able to choose a compromise between the performance of the policy and the cost to obtain the associated degree of observability of the problem. Finally, we show how the available space, surrounding an agent, influence the required degree of observability for near-optimal solution.