Connectionist learning procedures
Artificial Intelligence
Visualizing processes in neural networks
IBM Journal of Research and Development
Adaptivity in agent-based routing for data networks
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Gradient descent for general reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning sequences of actions in collectives of autonomous agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Coordination and Learning in Multirobot Systems
IEEE Intelligent Systems
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The Dynamic Selection of Coordination Mechanisms
Autonomous Agents and Multi-Agent Systems
Collectives and Design Complex Systems
Collectives and Design Complex Systems
Simulation and Visualization of a Market-Based Model for Logistics Management in Transportation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Handling Communication Restrictions and Team Formation in Congestion Games
Autonomous Agents and Multi-Agent Systems
Distributed agent-based air traffic flow management
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Regulating air traffic flow with coupled agents
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Efficient evaluation functions for evolving coordination
Evolutionary Computation
Using two main arguments in agent negotiation
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
A multiagent approach to managing air traffic flow
Autonomous Agents and Multi-Agent Systems
Evolving distributed resource sharing for cubesat constellations
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A decision-theoretic characterization of organizational influences
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
| Hi-index | 0.00 |
In many multi-agent learning problems, it is difficult to determine, a priori, the agent reward structure that will lead to good performance. This problem is particularly pronounced in continuous, noisy domains ill-suited to simple table backup schemes commonly used in TD(λ)/Q-learning. In this paper, we present a new reward evaluation method that provides a visualization of the tradeoff between coordination among the agents and the difficulty of the learning problem each agent faces. This method is independent of the learning algorithm and is only a function of the problem domain and the agents' reward structure. We then use this reward property visualization method to determine an effective reward without performing extensive simulations. We test this method in both a static and a dynamic multi-rover learning domain where the agents have continuous state spaces and where their actions are noisy (e.g., the agents' movement decisions are not always carried out properly). Our results show that in the more difficult dynamic domain, the reward efficiency visualization method provides a two order of magnitude speedup in selecting a good reward. Most importantly it allows one to quickly create and verify rewards tailored to the observational limitations of the domain.