Abstract
We show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking controller from a centralized MPC-based one, we achieve the best of both worlds: the neural controllers have high performance (on par with the MPC controllers) and high efficiency. Our experimental results demonstrate the sophisticated nature of the distributed controllers we learn. In particular, the neural controllers are capable of achieving myriad flocking-oriented control objectives, including flocking formation, collision avoidance, obstacle avoidance, predator avoidance, and target seeking. Moreover, they generalize the behavior seen in the training data to achieve these objectives in a significantly broader range of scenarios. In terms of verification of our neural flocking controller, we use a form of statistical model checking to compute confidence intervals for its convergence rate and time to convergence.
Chapter PDF
Similar content being viewed by others
Keywords
References
Bouabdallah, S.: Design and control of quadrotors with application to autonomous flying (2007)
Camacho, E.F., Bordons Alba, C.: Model Predictive Control. Springer (2007)
Chollet, F., et al.: Keras (2015), https://github.com/keras-team/keras.git
Godoy, J., Karamouzas, I., Guy, S.J., Gini, M.: Moving in a crowd: Safe and efficient navigation among heterogeneous agents. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. pp. 294–300. IJCAI’16, AAAI Press (2016)
Grosu, R., Peled, D., Ramakrishnan, C.R., Smolka, S.A., Stoller, S.D., Yang, J.: Using statistical model checking for measuring systems. In: 6th International Symposium, ISoLA 2014. Corfu, Greece (Oct 2014)
Hérault, T., Lassaigne, R., Magniette, F., Peyronnet, S.: Approximate probabilistic model checking. In: Steffen, B., Levi, G. (eds.) Verification, Model Checking, and Abstract Interpretation. pp. 73–84. Springer Berlin Heidelberg, Berlin, Heidelberg (2004)
Kahn, G., Villaflor, A., Pong, V., Abbeel, P., Levine, S.: Uncertainty-aware reinforcement learning for collision avoidance. arXiv preprint arXiv:1702.01182. pp. 1–12 (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015)
La, H.M., Lim, R., Sheng, W.: Multirobot cooperative learning for predator avoidance. IEEE Transactions on Control Systems Technology 23(1), 52–63 (2015)
Larsen, K.G., Legay, A.: Statistical model checking: Past, present, and future. In: 6th International Symposium, ISoLA 2014. Corfu, Greece (Oct 2014)
Mehmood, U., Paoletti, N., Phan, D., Grosu, R., Lin, S., Stoller, S.D., Tiwari, A., Yang, J., Smolka, S.A.: Declarative vs rule-based control for flocking dynamics. In: Proceedings of SAC 2018, 33rd Annual ACM Symposium on Applied Computing. pp. 816–823 (2018)
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York, NY, USA, second edn. (2006)
Olfati-Saber, R.: Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transactions on automatic control 51(3), 401–420 (2006)
Pfeiffer, M., Schaeuble, M., Nieto, J.I., Siegwart, R., Cadena, C.: From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots. In: 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, Singapore, May 29 - June 3, 2017. pp. 1527–1533 (2017)
Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)
Reynolds, C.W.: Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Comput. Graph. 21(4) (Aug 1987)
Reynolds, C.W.: Steering behaviors for autonomous characters. In: Proceedings of Game Developers Conference 1999. pp. 763–782 (1999)
Shimada, K., Bentley, P.: Learning how to flock: Deriving individual behaviour from collective behaviour with multi-agent reinforcement learning and natural evolution strategies. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 169–170. ACM (2018)
Zhan, J., Li, X.: Flocking of multi-agent systems via model predictive control based on position-only measurements. IEEE Transactions on Industrial Informatics 9(1), 377–385 (2013)
Zhang, H.T., Cheng, Z., Chen, G., Li, C.: Model predictive flocking control for second-order multi-agent systems with input constraints. IEEE Transactions on Circuits and Systems I: Regular Papers 62(6), 1599–1606 (2015)
Zhang, T., Kahn, G., Levine, S., Abbeel, P.: Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search. In: 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, Stockholm, Sweden, May 16-21, 2016. pp. 528–535 (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2020 The Author(s)
About this paper
Cite this paper
Mehmood, U., Roy, S., Grosu, R., Smolka, S.A., Stoller, S.D., Tiwari, A. (2020). Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers. In: Goubault-Larrecq, J., König, B. (eds) Foundations of Software Science and Computation Structures. FoSSaCS 2020. Lecture Notes in Computer Science(), vol 12077. Springer, Cham. https://doi.org/10.1007/978-3-030-45231-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-45231-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-45230-8
Online ISBN: 978-3-030-45231-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)