5 research outputs found
Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks
Recent developments in Deep Learning are noteworthy when it comes to learning the probability distribution of points through neural networks, and one of the crucial parts for such progress is because of Generative Adversarial Networks (GANs). In GANs, two neural networks, Generator and Discriminator, compete amongst each other to learn the probability distribution of points in visual pictures. A lot of research has been conducted to overcome the challenges of GANs which include training instability, mode collapse and vanishing gradient. However, there was no significant proof found on whether modern techniques consistently outperform vanilla GANs, and it turns out that different advanced techniques distinctively perform on different datasets. In this thesis, we propose two neuroevolutionary training techniques for deep convolutional GANs. We evolve the deep GANs architecture in low data regime. Using Fréchet Inception Distance (FID) score as the fitness function, we select the best deep convolutional topography generated by the evolutionary algorithm. The parameters of the best-selected individuals are maintained throughout the generations, and we continue to train the population until individuals demonstrate convergence. We compare our approach with the Vanilla GANs, Deep Convolutional GANs and COEGAN. Our experiments show that an evolutionary algorithm-based training technique gives a lower FID score than those of benchmark models. A lower FID score results in better image quality and diversity in the generated images
Evolutionary Game Theory Squared: Evolving Agents in Endogenously Evolving Zero-Sum Games
The predominant paradigm in evolutionary game theory and more generally
online learning in games is based on a clear distinction between a population
of dynamic agents that interact given a fixed, static game. In this paper, we
move away from the artificial divide between dynamic agents and static games,
to introduce and analyze a large class of competitive settings where both the
agents and the games they play evolve strategically over time. We focus on
arguably the most archetypal game-theoretic setting -- zero-sum games (as well
as network generalizations) -- and the most studied evolutionary learning
dynamic -- replicator, the continuous-time analogue of multiplicative weights.
Populations of agents compete against each other in a zero-sum competition that
itself evolves adversarially to the current population mixture. Remarkably,
despite the chaotic coevolution of agents and games, we prove that the system
exhibits a number of regularities. First, the system has conservation laws of
an information-theoretic flavor that couple the behavior of all agents and
games. Secondly, the system is Poincar\'{e} recurrent, with effectively all
possible initializations of agents and games lying on recurrent orbits that
come arbitrarily close to their initial conditions infinitely often. Thirdly,
the time-average agent behavior and utility converge to the Nash equilibrium
values of the time-average game. Finally, we provide a polynomial time
algorithm to efficiently predict this time-average behavior for any such
coevolving network game.Comment: To appear in AAAI 202