Reinforcement Learning

Reinforcement learning library, based on the Quality learning technique

> This package contains all the necessary(Auth, User Profile, Bank, Notifications, Language, Referral and Reward, QR, etc..) controllers, helpers and constants for Digipay Applications(Customer/Merchant/Agent).

A collection of various reinforcement learning solver. The library is an object-oriented approach (baked with Typescript) and tries to deliver simplified interfaces that make using the algorithms pretty simple.

Reinforcement learning using Markov Decision Processes

AmityEko Packaged Business Capabilities - Rewards and Recognitions client

Library implementing the double-q-learning algorithm.

A reinforcement learning environment for self-driving cars in the browser.

Data analysis model package without any dependencies

AmityEko Packaged Business Capabilities - Rewards and Recognitions moderation

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A Neural Network for Reinforcement Learning in TypeScript

Kenkai WEB SDK (JS/TS)

Library implementing the q-learning algorithm and several exploration algorithms.

Core interfaces for rl-js: Reinforcement Learning in JavaScript

Reinforcement learning in javascript

With this RESTful API you can integrate a global reward or incentive program into your app or platform. If you have any questions or if you'd like to receive your own credentials, please contact us at devsupport@tangocard.com.

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An API standard for single-agent reinforcement learning environments

Reinforcement Learning library