Q-RTS: a real-time swarm intelligence based on multi-agent Q-learning

The authors introduce a novel approach for swarm reinforcement learning that extends the standard Q-learning to multi-agent systems. State-of-the-art methods implement a knowledge sharing mechanism between the agents that is triggered by the episodes succession. This causes an intrinsic limit in the convergence speed of the algorithms. They overcame this issue by developing a Q-learning real-time swarm algorithm (Q-RTS), which is iteration-based and suitable for real-time systems. Q-RTS was tested in different environments and compared to other related methods in the literature. They obtained positive results in terms of learning time and scalability, i.e. achieving a speed-up factor of at least 1.49 with respect to standard Q-learning. Moreover, Q-RTS shows enhanced learning performance as the environments complexity increases.