Pinned Repositories
16899_ACRL
Repo for Adaptive Control and Reinforcement Learning work
active_learning_for_rsirl
Paper code for "Active Learning for Risk-Sensitive Inverse Reinforcement Learning". Available at https://arxiv.org/abs/1909.07843
Adaptive_Optimal_Control
Implementation of a paper on adaptive optimal control (solving ARE) based on policy iterations
Adversarial-Reinforcement-Learning
Bang-Bang-Control
A project on implementing optimal control to minimum time constrained input for non linear systems.
barc
Main branch for BARC related code
Courses
Courses taken during uni time
d2l-pytorch
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Data-Driven-Reachability-Analysis
sc2GAN-FE
lijitaonuaa5's Repositories
lijitaonuaa5/Data-Driven-Reachability-Analysis
lijitaonuaa5/sc2GAN-FE
lijitaonuaa5/16899_ACRL
Repo for Adaptive Control and Reinforcement Learning work
lijitaonuaa5/barc
Main branch for BARC related code
lijitaonuaa5/Courses
Courses taken during uni time
lijitaonuaa5/d2l-pytorch
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
lijitaonuaa5/data_driven_control
lijitaonuaa5/deeplearning_ai_books
deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）
lijitaonuaa5/diagram
lijitaonuaa5/drake
Model-based design and verification for robotics.
lijitaonuaa5/Efficient-motion-planning
To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles
lijitaonuaa5/helperOC
lijitaonuaa5/homework
Assignments for CS294-112.
lijitaonuaa5/Lane-Change-CBF
Rule-Based Safety-Critical Control Design with Application to Autonomous Lane Change
lijitaonuaa5/learning-based-rigid-tube-rmpc
This is the MATLAB code for tube robust MPC with uncertainty quantification
lijitaonuaa5/learning-qp
lijitaonuaa5/MSS
Marine Systems Simulator (MSS)
lijitaonuaa5/PARCIS
Parameterized Robust Control Invariant Sets (PARCIS)
lijitaonuaa5/pd_polLearn
Primal-Dual Policy Learning Simple Example
lijitaonuaa5/Pytorch_common_code
lijitaonuaa5/RADPBook
Source code for examples in Book "Robust Adaptive Dynamic Programming"
lijitaonuaa5/reachability-based_trajectory_safeguard
We use reachability to ensure the safety of a decision agent acting on a dynamic system in real-time. We compute the Forward Reachable Set offline and use it online to adjust any potentially unsafe decisions that cause a collision with an obstacle.
lijitaonuaa5/REFINE
lijitaonuaa5/reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
lijitaonuaa5/RMPC_SimpleTube
A simple robust MPC for linear systems with model mismatch: Balancing conservatism vs computational complexity
lijitaonuaa5/RTD
lijitaonuaa5/Safe-Reinforcement-Learning-for-Black-Box-Systems-Using-Reachability-Analysis
lijitaonuaa5/Underwater-Vehicle
ROV/AUV航行器控制中心 水下机器人（STM32 & Raspberry Pi）
lijitaonuaa5/VerifAI
VerifAI is a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components.
lijitaonuaa5/Verification-of-Car-Following-Reinforcement-Learning