TerrainRL Sim


Abstract

We provide \(88\) challenging simulation environments that range in difficulty. The difficulty in these \environments is linked not only to the number of dimensions in the action space but also to the task complexity. Using more complex and accurate simulations will help push the field closer to creating human-level intelligence. Therefore, we are releasing a number of simulation \environments that include local egocentric visual perception. These \environments include randomly generated terrain which the \agent needs to learn to interpret via visual features. The library also provides simple mechanisms to create new environments with different \agent morphologies and the option to modify the distribution of generated terrain.

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TerrainRL


DeepLoco

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Acknowledgements

We thank the anonymous reviewers for their helpful feedback. This research was funded in part by an NSERC Discovery Grant (RGPIN-2015-04843).