Table of Links Abstract and 1. Introduction 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 Background & Related Work Background & Related Work Background & Related Work Method
3.1 Sampling Small Mutations
3.2 Policy
3.3 Value Network & Search
3.4 Architecture Method Method 3.1 Sampling Small Mutations 3.1 Sampling Small Mutations 3.2 Policy 3.2 Policy 3.3 Value Network & Search 3.3 Value Network & Search 3.4 Architecture 3.4 Architecture Experiments
4.1 Environments
4.2 Baselines
4.3 Ablations Experiments 4.1 Environments 4.1 Environments 4.2 Baselines 4.2 Baselines 4.3 Ablations 4.3 Ablations Conclusion, Acknowledgments and Disclosure of Funding, and References Conclusion, Acknowledgments and Disclosure of Funding, and References Conclusion, Acknowledgments and Disclosure of Funding, and References Appendix Appendix A. Mutation Algorithm A. Mutation Algorithm B. Context-Free Grammars B. Context-Free Grammars C. Sketch Simulation C. Sketch Simulation D. Complexity Filtering D. Complexity Filtering E. Tree Path Algorithm E. Tree Path Algorithm F. Implementation Details 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. 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). Authors: 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). This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv available on arxiv

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From Program to Sketch: Modeling Non-Deterministic Observations in Code Generation

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