Swarm reinforcement learning algorithms based on Sarsa method

We recently proposed swarm reinforcement learning algorithms in which multiple agents are prepared and they all learn concurrently with two learning strategies: individual learning and learning through exchanging information. In the proposed swarm reinforcement learning algorithms, Q-learning method was used for the individual learning. However, there have been proposed several reinforcement learning methods, and it is required to investigate how to apply these methods to swarm reinforcement learning algorithms and evaluate their performance. In this paper, we propose swarm reinforcement learning algorithms based on Sarsa method in order to obtain an optimal policy rapidly for problems with negative large rewards. The proposed algorithm is applied to a shortest path problem, and its performance is examined through numerical experiments.