COOPERATIVE LEARNING BY POLICY-SHARING IN MULTIPLE AGENTS
Cybernetics and Systems
A multiagent cooperative learning algorithm
CSCWD'06 Proceedings of the 10th international conference on Computer supported cooperative work in design III
Cooperation between multiple agents based on partially sharing policy
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
From cognition to docition: The teaching radio paradigm for distributed & autonomous deployments
Computer Communications
Docitive networks: an emerging paradigm for dynamic spectrum management
IEEE Wireless Communications
Parallel reinforcement learning with linear function approximation
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Fuzzy-Q knowledge sharing techniques with expertness measures: comparison and analysis
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Comparison and analysis of expertness measure in knowledge sharing among robots
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Non-reciprocating Sharing Methods in Cooperative Q-Learning Environments
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Distributed relational temporal difference learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Multi-criteria expertness based cooperative Q-learning
Applied Intelligence
Reduction of state space in reinforcement learning by sensor selection
Artificial Life and Robotics
METAL: A framework for mixture-of-experts task and attention learning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Active noise control system via multi-agent credit assignment
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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By using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be gained if the learning agent can extract proper rules from the other agents' knowledge for its own requirements. One possible way to do this is to have the learner assign some expertness values (intelligence level values) to the other agents and use their knowledge accordingly. Some criteria to measure the expertness of the reinforcement learning agents are introduced. Also, a new cooperative learning method, called weighted strategy sharing (WSS) is presented. In this method, each agent measures the expertness of its teammates and assigns a weight to their knowledge and learns from them accordingly. The presented methods are tested on two Hunter-Prey systems. We consider that the agents are all learning from each other and compare them with those who cooperate only with the more expert ones. Also, the effect of communication noise, as a source of uncertainty, on the cooperative learning method is studied. Moreover, the Q-table of one of the cooperative agents is changed randomly and its effects on the presented methods are examined