In many complex dynamical systems, artificial or natural, one can observe selforganization 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 intrinsicallymotivated 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.
The focus of the presented paper is on formulating the automated discovery of self-organized patterns in high-dimensional dynamic systems. The introduced framework uses cellular automata (game of life) as a testbed for experimentation and evaluation and existing machine learning algorithms (POP-IMGEPs). The goal of the paper is to show that these algorithms can be used to discover and represent features of patterns. Moreover, an extension of SOTA algorithms is introduced and several approaches to define goal space representations are compared. 
Overall, I have the impression this is an interesting paper that could be accepted to ICLR. The idea of applying IMGEPs to explore parameters of a dynamic system is novel and interesting, which could also simulate further research in this field. Furthermore, the paper well-written, technically sound, and the results are interesting. The overall contribution of the paper is in applying IMGEP algorithms to exploring parameters of dynamic systems and in comparing different algorithms along with an extensive set of experiments. As a point of criticism, a lot of (interesting) material was pushed to the Appendix. Resolving the references makes reading the paper harder. Moreover, given that this paper has more than 35 pages appendix material, it seems this work would better be suited for a journal as for a conference. There is a reason for papers to have a page limit and this work circumvents this limit by presenting a lot of additional material. Therefore, I am not willing to strongly support this work. 
Specific Comments: 
- Section 3.1: It is not clear how the initial system state is established. In Section 3.1. the text states that 'parameters are randomly sampled and explored' before the process starts, but it is not clear why a random sampling is used and what this means for the subsequent sampling. Later in the text (3.3) it becomes more clear, but here this appears too unclear.
-  Section 3.1: "distribution over a hypercube in \mathcal{T} chosen to be large enough to bias exploration towards the frontiers of known goals to incentivize diversity." This sentence is not clear and needs more details. How is the distribution chosen exactly?
- Section 3.2 appears a bit repetitive and could be more concise. I don't think it is necessary here to contrast manual vs learned features of the goal space.
- Section 3.2 (P3): the last sentence of this paragraph reads as if there exists no approaches for VEAs in online settings. This should be toned down or backed up by a reference.
- Section 3.2: (last sentence): it is not clear how the history is used exactly to train the network. Which strategy is used to sample from the history of observations?
- Section 3.3: What is meant by "The CPPNs are used of the parameters \{theta}"? The details provided after this sentence are not clear and need more details. 
- Section 4.2: Please provide more details what "very large" dataset means.
- Section 4.2: 'HGS algorithm' is not defined.
- Section 5: It seems unnecessary to explain what t-SNE does as a method.