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Implementation of Li's Learning to Optimize paper using Pytorch
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zhusheldon/L2O-PytorchImp
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Code for Learning to Optimize This is a Python re-implementation of the method described in our paper, which can be found at https://arxiv.org/abs/1606.01885 It is based on the Guided Policy Search implementation (https://github.com/cbfinn/gps). Copyright (C) 2017 Ke Li, Jitendra Malik This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. =========================================================================================================================================== Run compile_proto.sh first before running run_lto.sh
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