{"id":19382855,"url":"https://github.com/locuslab/differentiable-mpc","last_synced_at":"2025-04-09T12:08:43.097Z","repository":{"id":44361350,"uuid":"154850262","full_name":"locuslab/differentiable-mpc","owner":"locuslab","description":null,"archived":false,"fork":false,"pushed_at":"2023-02-08T19:48:51.000Z","size":5787,"stargazers_count":267,"open_issues_count":4,"forks_count":52,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-04-02T10:14:50.503Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/locuslab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.mit","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-10-26T14:49:49.000Z","updated_at":"2025-03-24T02:54:17.000Z","dependencies_parsed_at":"2024-11-10T09:36:51.286Z","dependency_job_id":null,"html_url":"https://github.com/locuslab/differentiable-mpc","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/locuslab%2Fdifferentiable-mpc","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/locuslab%2Fdifferentiable-mpc/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/locuslab%2Fdifferentiable-mpc/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/locuslab%2Fdifferentiable-mpc/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/locuslab","download_url":"https://codeload.github.com/locuslab/differentiable-mpc/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248036067,"owners_count":21037092,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-10T09:23:37.011Z","updated_at":"2025-04-09T12:08:43.078Z","avatar_url":"https://github.com/locuslab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Differentiable MPC for End-to-end Planning and Control\n\nThis repository is by [Brandon Amos](http://bamos.github.io),\nIvan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots,\nand [J. Zico Kolter](http://zicokolter.com)\nand contains the [PyTorch](https://pytorch.org) source code to\nreproduce the experiments in our NIPS 2018 paper\n[Differentiable MPC for End-to-end Planning and Control](https://arxiv.org/abs/1810.13400).\n\nThe PyTorch implementation of the fast and differentiable MPC solver\nwe developed for this work is available as a standalone library at\n[locuslab/mpc.pytorch](https://locuslab.github.io/mpc.pytorch/).\n\nIf you find this repository helpful in your publications,\nplease consider citing our paper.\n\n```\n@article{amos2018differentiable,\n  title={{Differentiable MPC for End-to-end Planning and Control}},\n  author={Brandon Amos and Ivan Jimenez and Jacob Sacks and Byron Boots and J. Zico Kolter},\n  booktitle={{Advances in Neural Information Processing Systems}},\n  year={2018}\n}\n```\n\n## Setup and Dependencies\n\n+ Python/numpy/[PyTorch](https://pytorch.org)\n+ [locuslab/mpc.pytorch](https://github.com/locuslab/mpc.pytorch)\n\n# LQR Imitation Learning Experiments\n\nFrom within the `imitation_lqr` directory:\n1. `train.py` is the main training script for the experiment \n   in Section 5.3.\n\n# Non-Convex Imitation Learning Experiments\n\nFrom within the `imitation_nonconvex` directory:\n1. `make_dataset.py` should be run to create a dataset of trajectories\n   for each environment.\n2. `il_exp.py` is the main training script for each experiment.\n3. `run-pendulum-cartpole.sh` runs all of the experiments for the\n   pendulum and cartpole environments in Section 5.3.\n3. `run-complex-pendulum.sh` runs all of the experiments for the\n   non-realizable pendulum environment in Section 5.4.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flocuslab%2Fdifferentiable-mpc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flocuslab%2Fdifferentiable-mpc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flocuslab%2Fdifferentiable-mpc/lists"}