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AirSim - Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research
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AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.
Our goal is to develop AirSim as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. For this purpose, AirSim also exposes APIs to retrieve data and control vehicles in a platform independent way.
Related Projects
ai-resources - Selection of resources to learn Artificial Intelligence / Machine Learning / Statistical Inference / Deep Learning / Reinforcement Learning
Update April 2017: It’s been almost a year since I posted this list of resources, and over the year there’s been an explosion of articles, videos, books, tutorials etc on the subject — even an explosion of ‘lists of resources’ such as this one. It’s impossible for me to keep this up to date. However, the one resource I would like to add is https://ml4a.github.io/ (https://github.com/ml4a) led by Gene Kogan. It’s specifically aimed at artists and the creative coding community. This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy). As someone who has no formal background in Computer Science (but has been programming for many years), the language, notation and concepts of ML/SI/DL and even CS was completely alien to me, and the learning curve was not only steep, but vertical, treacherous and slippery like ice.
UETorch - A Torch plugin for Unreal Engine 4.
UETorch is an Unreal Engine 4 plugin that adds support for embedded Lua/Torch scripts in the game engine loop, and a set of Lua APIs for providing user input, taking screenshots and segmentation masks, controlling game state, running faster than real time, etc. Torch is an AI Research platform that is focused on deep learning. UETorch strongly leverages the sparsely documented ScriptPlugin plugin provided with Unreal Engine 4.Some recent research done using the UETorch platform is detailed in this paper "Learning Physical Intuition of Block Towers by Example" where we explore the ability of deep feed-forward models to learn intuitive physics.
ELF - An End-To-End, Lightweight and Flexible Platform for Game Research
ELF is an Extensive, Lightweight and Flexible platform for game research, in particular for real-time strategy (RTS) games. On the C++-side, ELF hosts multiple games in parallel with C++ threading. On the Python side, ELF returns one batch of game state at a time, making it very friendly for modern RL. In comparison, other platforms (e.g., OpenAI Gym) wraps one single game instance with one Python interface. This makes concurrent game execution a bit complicated, which is a requirement of many modern reinforcement learning algorithms. Besides, ELF now also provides a Python version for running concurrent game environments, by Python multiprocessing with ZeroMQ inter-process communication. See ./ex_elfpy.py for a simple example.
gaming cpp artificial-intelligence deep-learning neural-network platform reinforcement-learningawesome-artificial-intelligence - A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers. Contributions most welcome.
machine-learning machine-intelligence artificial-intelligence reinforcement-learning intelligent-systems deep-learning intelligent-machinesvisionworkbench - The NASA Vision Workbench is a general purpose image processing and computer vision library developed by the Autonomous Systems and Robotics (ASR) Area in the Intelligent Systems Division at the NASA Ames Research Center
The NASA Vision Workbench is a general purpose image processing and computer vision library developed by the Autonomous Systems and Robotics (ASR) Area in the Intelligent Systems Division at the NASA Ames Research Center.
Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET
The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.
machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpegml-agents - Unity Machine Learning Agents
Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.
reinforcement-learning unity3d deep-learning unity deep-reinforcement-learning neural-networksHow_to_simulate_a_self_driving_car - This is the code for "How to Simulate a Self-Driving Car" by Siraj Raval on Youtube
This is the code for this video on Youtube by Siraj Raval. We're going to use Udacity's self driving car simulator as a testbed for training an autonomous car. You need a anaconda or miniconda to use the environment setting.
ai-deadlines - :alarm_clock: AI conference deadline countdowns
Countdown timers to keep track of a bunch of CV/NLP/ML/AI conference deadlines.
deep-learning deep-neural-networks machine-learning computer-vision artificial-intelligence natural-language-processing roboticsXLearning - AI on Hadoop
XLearning is a convenient and efficient scheduling platform combined with the big data and artificial intelligence, support for a variety of machine learning, deep learning frameworks. XLearning is running on the Hadoop Yarn and has integrated deep learning frameworks such as TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning has the satisfactory scalability and compatibility.Besides the distributed mode of TensorFlow and MXNet frameworks, XLearning supports the standalone mode of all deep learning frameworks such as Caffe, Theano, PyTorch. Moreover, XLearning allows the custom versions and multi-version of frameworks flexibly.
hadoop tensorflow caffe mxnet yarnThe RoboCup Soccer Simulator
The RoboCup Soccer Simulator is a research and educational tool for multi-agent systems and artificial intelligence. It enables for two teams of 11 simulated autonomous robotic players to play soccer (football).
OpenCog - Framework to build Artificial Intelligence Programs
The OpenCog Framework is a platform to build and share artificial intelligence programs. It includes components for procedural and declarative knowledge representation (AtomSpace), task scheduling (CogServer), AI algorithm containers (MindAgents), connectors to instant messaging and virtual world systems, and other components. MindAgents and other add-ons explore a wide variety of AI techniques including evolutionary program learning (MOSES), natural language processing, and others.
artificial-intelligence neural-network nlpautonomous-rc-car - Autonomous RC car using Raspberry Pi and ANN
This project aims to build an autonomous rc car using supervised learning of a neural network with a single hidden layer. We have not used any Machine Learning libraries since we wanted to implement the neural network from scratch to understand the concepts better. We have modified a remote controlled car to remove the dependency on the RF remote controller. A Raspberry Pi controls the DC motors via an L293D Motor Driver IC. You can find a post explaining this project in detail here. Here's a video of the car in action. We will be referring the DC motor controlling the left/right direction as the front motor and the motor controlling the forward/reverse direction as the back motor. Connect the BACK_MOTOR_DATA_ONE and BACK_MOTOR_DATA_TWO GPIO pins(GPIO17 and GPIO27) of the Raspberry Pi to the Input pins for Motor 1(Input 1, Input 2) and the BACK_MOTOR_ENABLE_PIN GPIO pin(GPIO22) to the Enable pin for Motor 1(Enable 1,2) in the L293D Motor Driver IC. Connect the Output pins for Motor 1(Output 1, Output 2) of the IC to the back motor.
rc-car neural-network raspberry-pi autonomous-car machine-learninggorgonia - Gorgonia is a library that helps facilitate machine learning in Go.
Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.
machine-learning artificial-intelligence neural-network computation-graph differentiation gradient-descent gorgonia deep-learning deeplearning deep-neural-networks automatic-differentiation symbolic-differentiation go-libraryConvNetJS - Javascript implementation of Neural networks
ConvNetJS is a Javascript implementation of Neural networks, It currently supports Common Neural Network modules, Classification (SVM/Softmax) and Regression (L2) cost functions, A MagicNet class for fully automatic neural network learning (automatic hyperparameter search and cross-validatations), Ability to specify and train Convolutional Networks that process images, An experimental Reinforcement Learning module, based on Deep Q Learning.
artificial-intelligence neural-networks machine-learning deep-learningApache Singa - Distributed Deep Learning Platform
SINGA is a distributed deep learning platform for big data analytics. It supports various deep learning models, and thus has the flexibility to allow users to customize the models that fit their business requirements. It provides a scalable architecture to train deep learning models from huge volumes of data and it makes the distributed training process transparent to users.
machine-learning artificial intelligence neural-networks distributedpapers - :paperclip: Summaries of papers on deep learning
Summaries of papers on deep learning.
deep-learning deep-neural-networks computer-vision machine-learning artificial-intelligencet81_558_deep_learning - Washington University (in St
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.
neural-network machine-learning tensorflow keras deeplearningUAV PathPlanner
The unmanned autonomous vehicle is one step before the flying car. We propose a radically new approach which builds on the decades of research in Artificial Intelligence that is designed for a car from the ground up.
AI-Programmer - Using artificial intelligence and genetic algorithms to automatically write programs
Read the research paper BF-Programmer: A Counterintuitive Approach to Autonomously Building Simplistic Programs Using Genetic Algorithms. AI-Programmer is an experiment with using artificial intelligence and genetic algorithms to automatically generate programs. Successfully created programs by the AI include: hello world, hello , addition, subtraction, reversing a string, fibonnaci sequence, 99 bottles of beer on the wall, and more. It's getting smarter. In short, it's an AI genetic algorithm implementation with self modifying code.
genetic-algorithms c-sharp fitness-score ai artificial-intelligence genetic-algorithm genetic-programming programming-language genome program-synthesisSocial Icons
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