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RL library based on algorithms from the book <A-introduction-to-reinforcement-learning>
Bilevel Optimization Algorithm
Bayes Net Toolbox for Matlab
Create a path planner that is able to navigate a car safely around a virtual highway
CS231n assignment & Notes & Slides
Deep Reinforcement Learning (DQN) based Self Driving Car Control with Vehicle Simulator
Gambit
A minimalist environment for decision-making in autonomous driving
Iterative Linear-Quadratic Games!
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.
lane change trajectories extracted from NGSIM
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
My blogs and code for machine learning. http://cnblogs.com/pinard
I'm gonna save my work in Github
Code for a multiple cooperative-mpc for multiple vehicles (esp. ambulance)
NGSIM I-80 dataset Leader - Follower Vehicle Trajectory Pairs
smoothing the NGSIM US-101 trajectory dataset using Savitzky-Golay Filter
An rllab environment for learning human driver models with imitation learning using NGSIM data
This repository contains my implementation of the programming assignments of Probabilistic Graphical Models delivered by Stanford University on Coursera
Python sample codes for robotics algorithms.
Python Implementation of Reinforcement Learning: An Introduction
Udacity Self-Driving Car Engineer Nanodegree: Quintic Polynomial Solver. & Paper 'Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame'
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.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.