BAT Q-LEARNING ALGORITHM

Cooperative q-learning approach allows multiple learners to learn independently and then share their q-values among each other using a q-value sharing strategy. a main problem with this approach is that the solutions of the learners may not converge to optimality, because the optimal q-values may not be found. another problem is that some cooperative algorithms perform very well with single-task problems, but quite poorly with multi-task problems. this paper proposes a new cooperative q-learning algorithm called the bat q-learning algorithm (bq-learning) that implements a q-value sharing strategy based on the bat algorithm. the bat algorithm is a powerful optimization algorithm that increases the possibility of finding the optimal q-values by balancing between the exploration and exploitation of actions by tuning the parameters of the algorithm. the bq-learning algorithm was tested using two problems: the shortest path problem (single-task problem) and the taxi problem (multi-task problem). the experimental results suggest that bq-learning performs better than single-agent q-learning and some well-known cooperative q-learning algorithms.