Brian-Deng's repositories
A-introduction-to-reinforcement-learning
RL library based on algorithms from the book <A-introduction-to-reinforcement-learning>
bnt
Bayes Net Toolbox for Matlab
CarND-Path-Planning-Project
Create a path planner that is able to navigate a car safely around a virtual highway
DRL_based_SelfDrivingCarControl
Deep Reinforcement Learning (DQN) based Self Driving Car Control with Vehicle Simulator
gambit
Gambit
highway-env
A minimalist environment for decision-making in autonomous driving
ilqgames
Iterative Linear-Quadratic Games!
irlmodelvalidation
This python package contains scripts needed to train IRL Driver models on HighD datasets. This code is accompanying the paper "Validating human driver models for interaction-aware automated vehicle controllers: A human factors approach - Siebinga, Zgonnikov & Abbink 2021" and should be used in combination with TraViA, a program for traffic data visualization and annotation.
LC_NGSIM
lane change trajectories extracted from NGSIM
machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
master-thesis-code
I'm gonna save my work in Github
mpc-multiple-vehicles
Code for a multiple cooperative-mpc for multiple vehicles (esp. ambulance)
NGSIM-trajectories
NGSIM I-80 dataset Leader - Follower Vehicle Trajectory Pairs
NGSIM-US-101-trajectory-dataset-smoothing
smoothing the NGSIM US-101 trajectory dataset using Savitzky-Golay Filter
Probabilistic-Graphical-Model
This repository contains my implementation of the programming assignments of Probabilistic Graphical Models delivered by Stanford University on Coursera
PythonRobotics
Python sample codes for robotics algorithms.
reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
Robotics-Path-Planning-04-Quintic-Polynomial-Solver
Udacity Self-Driving Car Engineer Nanodegree: Quintic Polynomial Solver. & Paper 'Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame'
Temporal_Difference_Learning_Path_Planning
When born, animals and humans are thrown into an unknown world forced to use their sensory inputs for survival. As they begin to understand and develop their senses they are able to navigate and interact with their environment. The process in which we learn to do this is called reinforcement learning. This is the idea that learning comes from a series of trial and error where there exists rewards and punishments for every action. The brain naturally logs these events as experiences, and decides new actions based on past experience. An action resulting in a reward will then be higher favored than an action resulting in a punishment. Using this concept, autonomous systems, such as robots, can learn about their environment in the same way. Using simulated sensory data from ultrasonic sensors, moisture sensors, encoders, shock sensors, pressure sensors, and steepness sensors, a robotic system will be able to make decisions on how to navigate through its environment to reach a goal. The robotic system will not know the source of the data or the terrain it is navigating. Given a map of an open environment simulating an area after a natural disaster, the robot will use model-free temporal difference learning with exploration to find the best path to a goal in terms of distance, safety, and terrain navigation. Two forms of temporal difference learning will be tested; off-policy (Q-Learning) and onpolicy (Sarsa). Through experimentation with several world map sizes, it is found that the off-policy algorithm, Q-Learning, is the most reliable and efficient in terms of navigating a known map with unequal states.