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Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing. (arXiv:2103.01009v3 [cs.LG] UPDATED)
Jan. 4, 2022, 2:10 a.m. | Charlie Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson
cs.LG updates on arXiv.org arxiv.org
Recent research has shown that graph neural networks (GNNs) can learn
policies for locomotion control that are as effective as a typical multi-layer
perceptron (MLP), with superior transfer and multi-task performance (Wang et
al., 2018; Huang et al., 2020). Results have so far been limited to training on
small agents, with the performance of GNNs deteriorating rapidly as the number
of sensors and actuators grows. A key motivation for the use of GNNs in the
supervised learning setting is their …
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