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Flatland is a simple, lightweight environment for fast prototyping andtesting of reinforcement learning agents. It is of lower complexity compared tosimilar 3D platforms (e.g. DeepMind Lab or VizDoom), but emulates physicalproperties of the real world, such as continuity, multi-modalpartially-observable states with first-person view and coherent physics. Wepropose to use it as an intermediary benchmark for problems related to LifelongLearning. Flatland is highly customizable and offers a wide range of taskdifficulty to extensively evaluate the properties of artificial agents. Weexperiment with three reinforcement learning baseline agents and show that theycan rapidly solve a navigation task in Flatland. A video of an agent acting inFlatland is available here: https://youtu.be/I5y6Y2ZypdA.

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