Towards generating complex programs represented as node-trees with reinforcement learning

Abstract

In this work we propose to use humanly pre-built functions which we refer to as nodes, to synthesize complex programs. As e.g. in the node-tree programming style of Houdini (sidefx.com), we propose to generate programs by concatenating nodes non-linearly to node-trees which allows for nesting functions inside functions. We implemented a reinforcement learning environment and performed tests with state-of-the-art reinforcement learning algorithms. We conclude, that automatically generating complex programs by generating node-trees is possible and present a new approach of injecting training samples into the reinforcement learning process.