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Model Predictive Control

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/google-research/dads

Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

https://github.com/google-research/pddm

Deep Dynamics Models for Learning Dexterous Manipulation

https://github.com/google-research/tiny-differentiable-simulator

Interactive Differentiable Simulation

https://github.com/hbzhang/AwesomeSelfDriving

Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving

https://github.com/hybridrobotics/car-racing

Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function

https://github.com/jeroaranda/thesis

Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems

https://github.com/jollyraven100/Quant_algorithmic-trading_and_More

Multi-Period Trading via Convex Optimization

https://github.com/jollyraven100/Trade-Ideas-and-Research-Reference

Multi-Period Trading via Convex Optimization

https://github.com/lucys0/HRL

Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

https://github.com/m-serra/action-inference-for-video-prediction-benchmarking

Deep Visual Foresight for Planning Robot Motion

https://github.com/mehdimashayekhi/Some-RL-Implementation

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/michaelsyao/Trade-Ideas-and-Research-Reference

Multi-Period Trading via Convex Optimization

https://github.com/monimoyb/pd_polLearn

Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks

https://github.com/nagaban2/nn_dynamics

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/natolambert/dynamicslearn

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/nhatch/nhatch.github.io

The Value of Planning for Infinite-Horizon Model Predictive Control

https://github.com/nicopao/CGM_prediction_data

MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas

https://github.com/paperstiger/MoE-public

Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts

https://github.com/pfnet-research/chainer-differentiable-mpc

Differentiable MPC for End-to-end Planning and Control

https://github.com/rvl-lab-utoronto/HyperCRL

Continual Model-Based Reinforcement Learning with Hypernetworks

https://github.com/ryanjulian/embed2learn

Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing

https://github.com/saferoboticslab/sharp

Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach

https://github.com/seuqaj114/paig

Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video

https://github.com/stavrosorf/ev2gym

A Simulation Tool for V2G Enabled Demand Response Based on Model Predictive Control

https://github.com/um-arm-lab/pytorch_mppi

RUMI: Rummaging Using Mutual Information

https://github.com/unstable-zeros/data-driven-sls

Data-driven Distributed and Localized Model Predictive Control

https://github.com/upb-lea/gem-control

Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control

https://github.com/wzjoriv/Hion

Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems

https://github.com/zhaoyi11/tcrl

Temporal Difference Learning for Model Predictive Control

https://github.com/zuoxingdong/VIN_PyTorch_Visdom

Learning from the Hindsight Plan -- Episodic MPC Improvement

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.