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This repository houses the code for "CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs" by Rohan Chitnis*, Tom Silver*, Beomjoon Kim, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Conference on Robot Learning (CoRL) 2020.

Paper: https://arxiv.org/abs/2007.13202

Video: https://www.youtube.com/watch?v=wTXt6djcAd4

Installation of dependencies:

To run, simply execute python main.py. This will, by default, start training in the TAMP NAMO environment. For other environments, planners, and/or methods, change family_to_run, solver_name, and/or approach_names respectively in settings.py.

Learned context-specific independences are packaged with this code, in the learned_csi/ directory. The core CAMP abstraction is implemented in approaches/modelbased.py, with supporting code in csi.py.

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