Innovative Approach Towards Cooperation Models for Multi-agent Reinforcement Learning (CMMARL)
Deepak Annasaheb Vidhate, Parag A. Kulkarni · Communications in computer and information science · 2016
We propose an innovative approach towards Cooperation Models for Multi-agent Reinforcement Learning (CMMARL) using reinforcement learning methods. Communication methods for reinforcement learning depend on multiagent scheme is proposed & implemented. Different cooperation methods for cooperative reinforcement learning based on expertness measure of each agent proposed here i.e. group method, dynamic method, goal-oriented method and expert agent method. Implementation results have demonstrated that the suggested communication and cooperation methods are able to accelerate the aggregation of the agents that accomplish best action strategies. This approach is developed for dynamic products availability in a three retailer shops in the market. Retailers can cooperate with each other and can get benefit from cooperative information by their own policies that accurately represent their goals and interests. The retailers are the learning agents in the problem and apply reinforcement learning to learn cooperatively from the situation. By making considerable theory on the dealer’s inventory strategy, refill period, and entry procedure of the customers, the problem turn out to be Markov decision process model thus facilitating to apply learning algorithms.