MAB.ai V2 has achieved 10x performance of MAB.ai V1 { https://github.com/TechTouchABI/MAB.ai } using state-of-the-art neural networks on a Intel Moviduis NCS USB stick, compared to our CPU and yours as well. To integrate Moviduis NCS with your CPU on Ubuntu 16.04 please visit https://github.com/movidius/ncsdk The Intel® Movidius™ Neural Compute software developer kit (NCSDK) is provided for users of the Intel® Movidius™ Neural Compute Stick (Intel® Movidius™ NCS). It includes software tools, an API, and examples, so developers can create software that takes advantage of the accelerated neural network capability provided by the Intel Movidius NCS hardware.
Check out the MAB.ai V2 performance on vimeo:https://vimeo.com/270219767
We have also added an AR scene to enhance the experience
Reinforcement learning is learning what to do - how to map situations to actions - so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation and, through that, all subsequent rewards.
Maximize the reward obtained by successively playing gamble machines (the ‘arms’ of the bandits) Invented in early 1950s by Robbins to model decision making under uncertainty when the environment is unknown The lotteries are unknown ahead of time
Each machine 𝑖 has a different (unknown) distribution law for rewards with (unknown) expectation 𝜇𝑖: Successive plays of the same machine yeald rewards that are independent and identically distributed Independence also holds for rewards across machines Reward = random variable 𝑋𝑖,𝑛 ; 1 ≤ 𝑖 ≤ 𝐾, 𝑛 ≥ 1 𝑖 = index of the gambling machine 𝑛 = number of plays 𝜇𝑖 = expected reward of machine 𝑖. A policy, or allocation strategy, 𝐴 is an algorithm that chooses the next machine to play based on the sequence of past plays and obtained rewards.
Clinical trials Adaptive routing in networks Advertising: what ad to put on a web-page? Economy: auctions Computation of Nash equilibria
- Download or Clone this repo
- Unzip the Assets zip file
- Start a new 3D project in Unity3D Game Engine
- Drag the Assets into the Assets Folder inside Unity3D
- Go the Build Settings make sure platform selected is UWP
- Enable XR and Vuforia in the player settings inspector
- Now that we have set up our environment lets open the ARscene and hit Play
Demo Application is also included in the zip file, if you don't have Unity3D installed on your machine. Just double click the MAB.ai.exe and your all set
This project was inspired by Unity Technologies project: https://github.com/Unity-Technologies/BanditDungeon