World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces

• URL: http://arxiv.org/abs/1912.13007v1
• Date: Mon, 30 Dec 2019 17:03:16 GMT
• Title: World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces
• Authors: Marwin H.S. Segler
• Abstract summary: We propose a formalism where the learner induces a world program by learning a dynamics model and the actions in graph-based compositional environments. We highlight a recent application, and propose a challenge for the community to assess world program-based planning.
• Score: 4.9023704104715256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Some of the most important tasks take place in environments which lack cheap and perfect simulators, thus hampering the application of model-free reinforcement learning (RL). While model-based RL aims to learn a dynamics model, in a more general case the learner does not know a priori what the action space is. Here we propose a formalism where the learner induces a world program by learning a dynamics model and the actions in graph-based compositional environments by observing state-state transition examples. Then, the learner can perform RL with the world program as the simulator for complex planning tasks. We highlight a recent application, and propose a challenge for the community to assess world program-based planning.

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