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Title: GitHub - DeepRoboticsResearch/model-based-papers: My reading list for model-based control

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Description: My reading list for model-based control. Contribute to DeepRoboticsResearch/model-based-papers development by creating an account on GitHub.

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Learning model-based planning from scratchhttps://arxiv.org/abs/1707.06170
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelshttps://arxiv.org/abs/1805.12114
SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learninghttps://arxiv.org/abs/1808.09105
Interaction Networks for Learning about Objects, Relations and Physicshttps://arxiv.org/abs/1612.00222
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluidshttps://arxiv.org/abs/1810.01566
Propagation Networks for Model-Based Control Under Partial Observationhttps://arxiv.org/abs/1809.11169
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulationhttps://arxiv.org/abs/1802.04325
A Compositional Object-Based Approach to Learning Physical Dynamicshttps://arxiv.org/abs/1612.00341
SPNets: Differentiable Fluid Dynamics for Deep Neural Networkshttps://arxiv.org/abs/1806.06094
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncinghttps://arxiv.org/abs/1808.03246
Graph networks as learnable physics engines for inference and controlhttps://arxiv.org/abs/1806.01242
Learning Latent Dynamics for Planning from Pixelshttps://arxiv.org/abs/1811.04551
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Imageshttps://arxiv.org/abs/1506.07365
Robust Locally-Linear Controllable Embeddinghttps://arxiv.org/abs/1710.05373
End-to-End Training of Deep Visuomotor Policieshttps://arxiv.org/abs/1504.00702
Unsupervised Learning for Physical Interaction through Video Predictionhttps://arxiv.org/abs/1605.07157
Deep Spatial Autoencoders for Visuomotor Learninghttps://arxiv.org/abs/1509.06113
Deep Visual Foresight for Planning Robot Motionhttps://arxiv.org/abs/1610.00696
Learning Plannable Representations with Causal InfoGANhttps://arxiv.org/abs/1807.09341
Learning Latent Dynamics for Planning from Pixelshttps://arxiv.org/abs/1811.04551
SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Controlhttps://arxiv.org/abs/1710.00489
MBMF: Model-Based Priors for Model-Free Reinforcement Learninghttps://arxiv.org/abs/1709.03153
Continuous deep q-learning with model-based accelerationhttps://arxiv.org/abs/1603.00748
Recurrent World Models Facilitate Policy Evolutionhttps://arxiv.org/abs/1809.01999
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Imageshttps://arxiv.org/abs/1506.07365
Robust Locally-Linear Controllable Embeddinghttps://arxiv.org/abs/1710.05373
Differentiable MPC for End-to-end Planning and Controlhttps://arxiv.org/abs/1810.13400
Path Integral Networks: End-to-End Differentiable Optimal Controlhttps://arxiv.org/abs/1706.09597
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learninghttp://www.roboticsproceedings.org/rss07/p08.pdf
SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learninghttps://arxiv.org/abs/1808.09105
Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Datahttps://arxiv.org/abs/1605.06432
Deep Kalman Filtershttps://arxiv.org/abs/1511.05121
Differentiable Particle Filters: End-to-End Learning with Algorithmic Priorshttps://arxiv.org/abs/1805.11122
QMDP-Net: Deep Learning for Planning under Partial Observabilityhttps://arxiv.org/abs/1703.06692
Generative Temporal Models with Spatial Memory for Partially Observed Environmentshttps://arxiv.org/abs/1804.09401
Learning Physical Dynamical Systems for Prediction and Control: A Surveyhttps://www.cs.princeton.edu/courses/archive/spring18/cos598B/public/projects/LiteratureReview/COS598B_spr2018_PhysicalDynamicalSystems.pdf
Learning Koopman Invariant Subspaces for Dynamic Mode Decompositionhttps://arxiv.org/abs/1710.04340
Deep Dynamical Modeling and Control of Unsteady Fluid Flowshttps://arxiv.org/abs/1805.07472
Deep learning for universal linear embeddings of nonlinear dynamicshttps://arxiv.org/abs/1712.09707
Data-driven discovery of Koopman eigenfunctions for controlhttps://arxiv.org/abs/1707.01146
Control-Limited Differential Dynamic Programminghttps://homes.cs.washington.edu/~todorov/papers/TassaICRA14.pdf
Learning Dynamical System Models from Datahttp://rll.berkeley.edu/deeprlcoursesp17/docs/week_3_lecture_1_dynamics_learning.pdf
EE263: Introduction to Linear Dynamical Systemshttp://ee263.stanford.edu/lectures.html
CS 287: Advanced Roboticshttps://people.eecs.berkeley.edu/~pabbeel/cs287-fa15/
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