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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-control

A reliable controller is critical for execution of safe and smooth maneuvers of an autonomous vehicle. The controller must be robust to external disturbances, such as road surface, weather, wind conditions, and so on. It also needs to deal with internal variations of vehicle sub-systems, including powertrain inefficiency, measurement errors, time delay, etc. These factors introduce issues in controller performance. In this paper, a feed-forward compensator is designed via a data-driven method to model and optimize the controller’s performance. Principal Component Analysis (PCA) is applied for extracting influential features, after which a Time Delay Neural Network is adopted to predict control errors over a future time horizon. Based on the predicted error, a feedforward compensator is then designed to improve control performance. Simulation results in different scenarios show that, with the help of with the proposed feedforward compensator, the maximum path tracking error and the steering wheel angle oscillation are improved by 44.4% and 26.7%, respectively.

data-driven-reachability-analysis

data_driven_control

deeplearning_ai_books

deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）

diagram

drake

Model-based design and verification for robotics.

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

helperoc

homework

Assignments for CS294-112.

lane-change-cbf

Rule-Based Safety-Critical Control Design with Application to Autonomous Lane Change

learning-based-rigid-tube-rmpc

This is the MATLAB code for tube robust MPC with uncertainty quantification

learning-qp

li

matlab-reinforcement-learning-pendulum

Q-Learning pendulum swing-up problem with animation as it's learning.

mppicontrol

Stochastic Optimal Control in Robotics Project

mr.li

mss

Marine Systems Simulator (MSS)

parcis

Parameterized Robust Control Invariant Sets (PARCIS)

pd_pollearn

Primal-Dual Policy Learning Simple Example

pytorch_common_code

q_learning_lqr_control

radpbook

Source code for examples in Book "Robust Adaptive Dynamic Programming"