Active perception and reinforcement learning
Proceedings of the seventh international conference (1990) on Machine learning
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Team-partitioned, opaque-transition reinforcement learning
Proceedings of the third annual conference on Autonomous Agents
Top-down search for coordinating the hierarchical plans of multiple agents
Proceedings of the third annual conference on Autonomous Agents
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multiagent Coordination with Learning Classifier Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Reinforcement learning agents with primary knowledge designed by analytic hierarchy process
Proceedings of the 2005 ACM symposium on Applied computing
Issues of law for software agents within virtual environments
Presence: Teleoperators and Virtual Environments - Special section: Legal, ethical, and policy issues associated with virtual environments and computer mediated reality
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A multi-agent system of evidential reasoning for intelligence analyses
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Cooperative behavior of agents based on potential field
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Dealing with errors in a cooperative multi-agent learning system
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
| Hi-index | 0.00 |
In this paper, we discuss Profit-sharing, an experience-baised reinforcement learning approach (which is similar to a Monte-Carlo based reinforcement learning method) that can be used to learn robust and effective actions within uncertain, dynamic, multi-agent systems. We introduce the cut-loop routine that discards looping behavior, and demonstrate its effectiveness empirically within a simplified NEO (non-combatant evacuation operation) domain. This domain consists of several agents which ferry groups of evacuees to one of several shelters. We demonstrate that the cut-loop routine makes the Profit-sharing approach adaptive and robust within a dynamic and uncertain domain, without the need for predefined knowledge or subgoals. We also compare it empirically with the popular Q-learning approach.