Abstract: b'Some of the most important tasks take place in environments which lack cheap\nand perfect simulators, thus hampering the application of model-free\nreinforcement learning (RL). While model-based RL aims to learn a dynamics\nmodel, in a more general case the learner does not know a priori what the\naction space is. Here we propose a formalism where the learner induces a world\nprogram by learning a dynamics model and the actions in graph-based\ncompositional environments by observing state-state transition examples. Then,\nthe learner can perform RL with the world program as the simulator for complex\nplanning tasks. We highlight a recent application, and propose a challenge for\nthe community to assess world program-based planning.<pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>'