Technical Note: \cal Q-Learning
Machine Learning
Average reward reinforcement learning: foundations, algorithms, and empirical results
Machine Learning - Special issue on reinforcement learning
A multiagent reinforcement learning algorithm using extended optimal response
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent learning in the presence of agents with limitations
Multiagent learning in the presence of agents with limitations
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A fuzzy constraint-based agent negotiation with opponent learning
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
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Multi-agent reinforcement learning is a challenging issue in artificial intelligence researches. In this paper, the reinforcement learning model and algorithm in multi-agent system simulation context are brought forward. We suggest and validate an opponent modeling learning to the problem of finding good policies for agents accommodated in an adversarial artificial world. The feature of the algorithm exhibits in that when in a multi-player adversarial environment the immediate reward depends on not only agent's action choose but also its opponent's trends. Experiment results show that the learning agent finds optimal policies in accordance with the reward functions provided