Optimal Tracking Control of a Nonlinear Multiagent System Using Q-Learning via Event-Triggered Reinforcement Learning

Round 1

Reviewer 1 Report

The paper is well-written and interesting. I did not see any issue any suggest it for publication in Entropy.

Author Response

Dear reviewer:

Thanks for your constructive comments concerning our manuscript "Optimal Tracking Control of Nonlinear Multiagent System Using Q-learning Via Event-triggered Reinforcement Learning". The comments are very helpful for revising and improving our paper. We have studied the report form and comments carefully. We hope that our paper can be published in Entropy.

Reviewer 2 Report

The authors discussed the offers an optimal control tracking method using an event triggered technique 1 and the internal reinforcement Q-learning algorithm to address the tracking controlling issue of 2 unknown nonlinear systems with multiple agents. Relying on the IRR formula, a Q-learning function 3 is calculated, and then the iteration IRQL method is developed. In contrast to mechanisms triggered 4 by time, an event-triggered algorithm reduces the rate of transmissions and computational load 5 since the controller may only be upgraded when the predetermined triggering circumstances are 6 met. In addition, in order to implement the suggested system, a neutral reinforce-critic-actor network 7 structure is created that may assess indices of performance and online learning of the event-triggering 8 mechanism. This paper is interesting and innovative. I recommend this paper for publication.

Author Response

Dear reviewer:

Thank you for your comments and suggestion about moderate English changes and the cited references. The comments and suggestions are all valuable very helpful for revising and improving our paper. We have studied comments carefully and have made correction which we hope meet with approval.