Bat q-learning algorithm

Abstract EN

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.