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In teacher-student framework, a more experienced agent (teacher) helpsaccelerate the learning of another agent (student) by suggesting actions totake in certain states. In cooperative multiagent reinforcement learning(MARL), where agents need to cooperate with one another, a student may fail tocooperate well with others even by following the teachers' suggested actions,as the polices of all agents are ever changing before convergence. When thenumber of times that agents communicate with one another is limited (i.e.,there is budget constraint), the advising strategy that uses actions as advicesmay not be good enough. We propose a partaker-sharer advising framework (PSAF)for cooperative MARL agents learning with budget constraint. In PSAF, eachQ-learner can decide when to ask for Q-values and share its Q-values. Weperform experiments in three typical multiagent learning problems. Evaluationresults show that our approach PSAF outperforms existing advising methods underboth unlimited and limited budget, and we give an analysis of the impact ofadvising actions and sharing Q-values on agents' learning.

A Q-values Sharing Framework for Multiagent Reinforcement Learning under Budget Constraint