Table of Links Abstract and 1. Introduction Background & Related Work Method 3.1 Sampling Small Mutations 3.2 Policy 3.3 Value Network & Search 3.4 Architecture Experiments 4.1 Environments 4.2 Baselines 4.3 Ablations Conclusion, Acknowledgments and Disclosure of Funding, and References \ Appendix A. Mutation Algorithm B. Context-Free Grammars C. Sketch Simulation D. Complexity Filtering E. Tree Path Algorithm F. Implementation Details C Sketch Simulation As mentioned in the main text, we implement the CSG2D-Sketch environment, which is the same as CSG2D with a hand-drawn sketch observation model. We do this to primarily show how this sort of a generative model can possibly be applied to a real-world task, and that observations do not need to \ \ be deterministic. Our sketch algorithm can be found in our codebase, and is based off the approach described in Wood et al. [39]. \ Our compiler uses Iceberg [16] and Google’s 2D Skia library to perform boolean operations on primitive paths. The resulting path consists of line and cubic bézier commands. We post-process these commands to generate sketches. For each command, we first add Gaussian noise to all points stated in those commands. For each line, we randomly pick a point near the 50% and 75% of the line, add Gaussian noise, and fit a Catmull-Rom spline [5]. For all curves, we sample random points at uniform intervals and fit Catmull-Rom splines. We have a special condition for circles, where we ensure that the start and end points are randomized to create the effect of the pen lifting off. Additionally we randomize the stroke thickness. \ Figure 10 shows the same program rendered multiple times using our randomized sketch simulator. \ :::info Authors: (1) Shreyas Kapur, University of California, Berkeley (srkp@cs.berkeley.edu); (2) Erik Jenner, University of California, Berkeley (jenner@cs.berkeley.edu); (3) Stuart Russell, University of California, Berkeley (russell@cs.berkeley.edu). ::: :::info This paper is available on arxiv under CC BY-SA 4.0 DEED license. ::: \Table of Links Abstract and 1. Introduction Background & Related Work Method 3.1 Sampling Small Mutations 3.2 Policy 3.3 Value Network & Search 3.4 Architecture Experiments 4.1 Environments 4.2 Baselines 4.3 Ablations Conclusion, Acknowledgments and Disclosure of Funding, and References \ Appendix A. Mutation Algorithm B. Context-Free Grammars C. Sketch Simulation D. Complexity Filtering E. Tree Path Algorithm F. Implementation Details C Sketch Simulation As mentioned in the main text, we implement the CSG2D-Sketch environment, which is the same as CSG2D with a hand-drawn sketch observation model. We do this to primarily show how this sort of a generative model can possibly be applied to a real-world task, and that observations do not need to \ \ be deterministic. Our sketch algorithm can be found in our codebase, and is based off the approach described in Wood et al. [39]. \ Our compiler uses Iceberg [16] and Google’s 2D Skia library to perform boolean operations on primitive paths. The resulting path consists of line and cubic bézier commands. We post-process these commands to generate sketches. For each command, we first add Gaussian noise to all points stated in those commands. For each line, we randomly pick a point near the 50% and 75% of the line, add Gaussian noise, and fit a Catmull-Rom spline [5]. For all curves, we sample random points at uniform intervals and fit Catmull-Rom splines. We have a special condition for circles, where we ensure that the start and end points are randomized to create the effect of the pen lifting off. Additionally we randomize the stroke thickness. \ Figure 10 shows the same program rendered multiple times using our randomized sketch simulator. \ :::info Authors: (1) Shreyas Kapur, University of California, Berkeley (srkp@cs.berkeley.edu); (2) Erik Jenner, University of California, Berkeley (jenner@cs.berkeley.edu); (3) Stuart Russell, University of California, Berkeley (russell@cs.berkeley.edu). ::: :::info This paper is available on arxiv under CC BY-SA 4.0 DEED license. ::: \