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The widespread adoption of robots will require a flexible and automated approach to robot design. Exploring the full space of all possible designs when creating a custom robot can prove to be computationally intractable, leading us to consider modular robots, composed of a common set of repeated components that can be reconfigured for each new task. But, conducting a combinatorial optimization process to create a specialized design for each new task and setting is computationally expensive, especially if the task changes frequently. In this work, our goal is to select mobile robot designs that will perform highest in a given environment under a known control policy, with the assumption that the selection process must be conducted for new environments frequently. We use deep reinforcement learning to create a neural network that, given a terrain map as an input, outputs the mobile robot designs deemed most likely to locomote successfully in that environment.