Complexity of bird song caused by adversarial imitation learning
- 13 Downloads
Biological evolution produces complexity through genetic variations based on randomness. In conventional communication or language simulation models, genetic variations based on randomness and fitness function rewarding task achievements play an important role in evolving communication signals to complex ones. However, it is known that not only genetic variations evolve communication but also imitative learning during developmental processes contributes to the evolution of communication. What we investigated here was to find a different principle of generating complexity which does not rely on the randomness or external environmental complexity but only on the learning processes in communication. Our hypothesis is that the contradictory learning mechanism we call the adversarial imitation learning can work to increase the complexity without relying on the random processes. To investigate our hypothesis, we implemented the adversarial imitation learning on a simulation where two agents interact with and imitate each other. Our results showed that the adversarial imitation learning causes chaotic dynamics and investigating the learning results in different types of interaction between the two; it was clarified that the adversarial imitation learning is necessary for the emergence of the chaotic time series.
KeywordsAdversarial imitation learning Complexity Chaos
This work was partially supported by JSPS KAKENHI Grant number JP18H05057.
- 1.Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence, 2nd edn, expanded 1992. University of Michigan PressGoogle Scholar
- 4.Froese T, Virgo N, Ikegami T (2011) Life as a process of open-ended becoming: analysis of a minimal model. In: Lenaerts T, Giacobini M, Bersini H, Bourgine P, Dorigo M, Doursat R (eds) Advances in artificial life, ECAL 2011: proceedings of the eleventh european conference on the synthesis and simulation of living systems. MIT Press, Cambridge, MA, pp 250–257Google Scholar
- 5.Miconi T (2008) Evosphere: evolutionary dynamics in a population of fighting virtual creatures. IEEE, pp 3066–3073Google Scholar
- 11.Steels L, Vogt P (1997) Grounding adaptive language games in robotic agents. In: Proceedings of the fourth European conference on artificial life, Brighton, UK, pp 474–482Google Scholar
- 16.Yamazaki S, Iizuka H, Yamamoto M (2018) Emergence of chaotic time series by adversarial imitation learning. MIT Press, pp 659–664Google Scholar
- 17.Robinson AJ, Frank Fallside (1987) The utility driven dynamic error propagation network. University of Cambridge Department of Engineering, CambridgeGoogle Scholar
- 18.Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: Proceedings of the 30th international conference on machine learning, ICML 2013, Atlanta, GA, USA, 16–21 June 2013, pp 1310–1318Google Scholar
- 20.Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets, pp 2672–2680Google Scholar
- 21.Moran N, Pollack JB (2017) Effects of cooperative and competitive coevolution on complexity in a linguistic prediction game, pp 298–205Google Scholar