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Gated Path Planning Networks
Lisa Lee · Emilio Parisotto · Devendra Singh Chaplot · Eric Xing · Ruslan Salakhutdinov

Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #134

Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture. Despite their effectiveness, they suffer from several disadvantages including training instability, random seed sensitivity, and other optimization problems. In this work, we reframe VINs as recurrent-convolutional networks which demonstrates that VINs couple recurrent convolutions with an unconventional max-pooling activation. From this perspective, we argue that standard gated recurrent update equations could potentially alleviate the optimization issues plaguing VIN. The resulting architecture, which we call the Gated Path Planning Network, is shown to empirically outperform VIN on a variety of metrics such as learning speed, hyperparameter sensitivity, iteration count, and even generalization. Furthermore, we show that this performance gap is consistent across different maze transition types, maze sizes and even show success on a challenging 3D environment, where the planner is only provided with first-person RGB images.

Author Information

Lisa Lee (Carnegie Mellon University)
Emilio Parisotto (Carnegie Mellon University)
Devendra Singh Chaplot (Carnegie Mellon University)
Eric Xing (Petuum Inc. and CMU)
Ruslan Salakhutdinov (Carnegie Mellen University)

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