Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems
arXiv:1908.06663
Abstract
In many complex dynamical systems, artificial or natural, one can observe self-organization of patterns emerging from local rules. Cellular automata, like the Game of Life (GOL), have been widely used as abstract models enabling the study of various aspects of self-organization and morphogenesis, such as the emergence of spatially localized patterns. However, findings of self-organized patterns in such models have so far relied on manual tuning of parameters and initial states, and on the human eye to identify interesting patterns. In this paper, we formulate the problem of automated discovery of diverse self-organized patterns in such high-dimensional complex dynamical systems, as well as a framework for experimentation and evaluation. Using a continuous GOL as a testbed, we show that recent intrinsically-motivated machine learning algorithms (POP-IMGEPs), initially developed for learning of inverse models in robotics, can be transposed and used in this novel application area. These algorithms combine intrinsically-motivated goal exploration and unsupervised learning of goal space representations. Goal space representations describe the interesting features of patterns for which diverse variations should be discovered. In particular, we compare various approaches to define and learn goal space representations from the perspective of discovering diverse spatially localized patterns. Moreover, we introduce an extension of a state-of-the-art POP-IMGEP algorithm which incrementally learns a goal representation using a deep auto-encoder, and the use of CPPN primitives for generating initialization parameters. We show that it is more efficient than several baselines and equally efficient as a system pre-trained on a hand-made database of patterns identified by human experts.
arXiv comment29 pages, 19 figure, ICLR 2020 conference paper, associated website: https://automated-discovery.github.io/
References in corpus (14)
- Adam: A Method for Stochastic Optimization
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Disentangling by Factorising
- Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots
- Recent Advances in Autoencoder-Based Representation Learning
- InfoVAE: Information Maximizing Variational Autoencoders
- Large-Scale Study of Curiosity-Driven Learning
- Visual Reinforcement Learning with Imagined Goals
- Wasserstein Auto-Encoders
- Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning
- Towards Deeper Understanding of Variational Autoencoding Models
- Curiosity Driven Exploration of Learned Disentangled Goal Spaces
- Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
- Exploration of Self-Propelling Droplets Using a Curiosity Driven Robotic Assistant