Technical Note: \cal Q-Learning
Machine Learning
Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Multi-agent reinforcement learning in Markov games
Multi-agent reinforcement learning in Markov games
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
Multi-Agent Reinforcement Learning: An Approach Based on the Other Agent's Internal Model
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Value-function reinforcement learning in Markov games
Cognitive Systems Research
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Recently, delayed reinforcement learning (RL) has been proposed as a strong method for learning in multi-agent systems (MASs). In this method, agents are concerned with the problem of discovering an optimal policy, a function mapping states to actions. The most popular RL technique, Q-learning, has been proven to produce an optimal policy under certain conditions. In this paper, we consider a multi-agent cooperation problem, and propose a multi-agent reinforcement learning method based on the other agents' actions. In our learning method, the agent under consideration observes other agents' action, and uses the minimax Q-learning using fuzzy state and fuzzy goal representation for updating fuzzy Q values.