Generating collective behavior of a robotic swarm using an attention agent with deep neuroevolution

1. Şahin E (2005) Swarm robotics: From sources of inspiration to domains of application. In: International workshop on swarm robotics, pp 10–20

2. Dorigo M et al (2014) Swarm robotics. Scholarpedia 9(1):14–63

   Article  Google Scholar 

3. Manuele B et al (2013) Swarm robotics: a review from the swarm engineering perspective. Swarm Intell 7(1):1–41

   Article  Google Scholar 

4. Nolfi S, Floreano D (2000) Evolutionary robotics: the biology, intelligence, and technology of self-organizing machines. MIT Press, Cambridge

   Google Scholar 

5. Baldassarre Gianluca et al (2003) Evolving mobile robots able to display collective behaviors. Artif Life 9(3):255–267

   Article  Google Scholar 

6. Onur Soysal et al (2007) Aggregation in swarm robotic systems: evolution and probabilistic control. Turk J Electr Eng Comput Sci 15(2):199–225

   Google Scholar 

7. Valerio Sperati et al (2011) Self-organised path formation in a swarm of robots. Swarm Intell 5:97–119

   Article  Google Scholar 

8. Alkilabi MHM et al (2017) Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies. Swarm Intell 11:185–209

   Article  Google Scholar 

9. Floreano Dario et al (2008) Neuroevolution: from architectures to learning. Evol Intell 1(1):47–62

   Article  Google Scholar 

10. Geoffrey Hinton et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29:82–97

    Article  Google Scholar 

11. Yichuan T et al (2012) Deep Lambertian networks. arXiv preprint arXiv:1206.6445

12. Mohammad Havaei et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31

    Article  Google Scholar 

13. David Silver et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484–489

    Article  Google Scholar 

14. Kun S et al (2019) A survey of deep reinforcement learning in video games. arXiv preprint arXiv:1912.10944

15. Nicolas H et al (2017) Emergence of locomotion behaviours in rich environments. arXiv preprint arXiv:1707.02286

16. Zhaoming X et al (2019) Iterative reinforcement learning based design of dynamic locomotion skills for cassie. arXiv preprint arXiv:1903.09537

17. Tabish Rashid et al (2020) Monotonic value function factorisation for deep multi-agent reinforcement learning. J Mach Learn Res 21(1):7234–7284

    MathSciNet  MATH  Google Scholar 

18. Terry J et al (2021) Pettingzoo: gym for multi-agent reinforcement learning. Ad Neural Inf Process Syst 34:15032–15043

    Google Scholar 

19. Hüttenrauch et al (2017) Guided deep reinforcement learning for swarm systems. arXiv preprint arXiv:1709.06011

20. Yixin H et al (2020) A multi-agent reinforcement learning method for swarm robots in space collaborative exploration. In: 2020 6th international conference on control, automation and robotics (ICCAR), pp 139–144

21. Lipton Zachary C (2018) The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3):31–57

    Article  Google Scholar 

22. Bolei Z et al (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2921–2929

23. Andrei K et al (2019) Xrai: better attributions through regions. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 4948–4957

24. Petroski SF et al (2017) Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567

25. Tim S et al (2017) Evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint arXiv:1703.03864

26. Tang Y et al (2020) Neuroevolution of self-interpretable agents. In: Proceedings of the 2020 genetic and evolutionary computation conference, pp 414–424

27. Spelke Elizabeth S, Kinzler Katherine D (2007) Core knowledge. Dev Sci 10(1):89–96

    Article  Google Scholar 

28. Yuezhang L et al (2018) An initial attempt of combining visual selective attention with deep reinforcement learning. arXiv preprint arXiv1811.04407

29. Mott A et al (2019) Towards interpretable reinforcement learning using attention augmented agents. Adv Neural Inf Process Syst 32

30. Ha D, Schmidhuber J (2018) World models. arXiv preprint arXiv:1803.10122

31. Risi S, Stanley KO (2019) Deep neuroevolution of recurrent and discrete world models. In: Proceedings of the genetic and evolutionary computation conference

32. Unity Technologies. Unity—game engine. https://unity3d.com/

33. Wierstra D et al (2014) Natural evolution strategies. J Mach Learn Res 15(1):949–980

    MathSciNet  MATH  Google Scholar 

34. He K et al (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026 –1034

35. John S et al (2017) Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347

36. Dosovitskiy A et al (2019) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

37. Liu Z et al (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10012–10022

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