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Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

Abstract : Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. Deep-Mind Lab or VizDoom), but emulates physical properties of the real world, such as continuity, multi-modal partially-observable states with first-person view and coherent physics. We propose to use it as an intermediary benchmark for problems related to Lifelong Learning. Flatland is highly customizable and offers a wide range of task difficulty to extensively evaluate the properties of artificial agents. We experiment with three reinforcement learning baseline agents and show that they can rapidly solve a navigation task in Flatland. A video of an agent acting in Flatland is available here:
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Contributor : David Filliat <>
Submitted on : Tuesday, December 11, 2018 - 5:27:01 PM
Last modification on : Thursday, January 21, 2021 - 9:26:01 AM
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1809.00510 (1).pdf
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  • HAL Id : hal-01951945, version 1



Hugo Caselles-Dupré, Louis Annabi, Oksana Hagen, Michael Garcia-Ortiz, David Filliat. Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning. Workshop on Continual Unsupervised Sensorimotor Learning, ICDL-EpiRob 2018, Sep 2018, Tokyo, Japan. ⟨hal-01951945⟩



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