Displaying 1 to 7 from 7 results

bayesian-bandit.js - Bayesian bandit implementation for Node and the browser.

  •    Javascript

This is an adaptation of the Bayesian Bandit code from Probabilistic Programming and Bayesian Methods for Hackers, specifically d3bandits.js. The code has been rewritten to be more idiomatic and also usable as a browser script or npm package. Additionally, unit tests are included.

rl - Reinforcement learning algorithms implemented using Keras and OpenAI Gym

  •    Python

This repository aims to contain the latest reinforcement learning algorithms implemented using Tensorflow, Keras and OpenAI Gym. Currently, A3C has been implemented.

rurel - Flexible, reusable reinforcement learning (Q learning) implementation in Rust

  •    Rust

Rurel is a flexible, reusable reinforcement learning (Q learning) implementation in Rust. There are two main traits you need to implement: rurel::mdp::State and rurel::mdp::Agent.




markovjs - Reinforcement Learning in JavaScript

  •    Javascript

This is a reference implementation of a basic reinforcement learning environment. It is intended as a playground for anyone interested in this field. This package exports a function that provides the environment you'll need to try your own problems.

markovjs-gridworld - gridworld implementation example for markovjs package

  •    Javascript

This is a game implementation example for the package markovjs. The game is Grid World, a popular toy problem for artificial intelligence search algorithms. Many people would say this game is just a simple graph maze. They would be wrong.

cherry - A PyTorch Library for Reinforcement Learning Research

  •    Python

Cherry is a reinforcement learning framework for researchers built on top of PyTorch. Unlike other reinforcement learning implementations, cherry doesn't implement a single monolithic interface to existing algorithms. Instead, it provides you with low-level, common tools to write your own algorithms. Drawing from the UNIX philosophy, each tool strives to be as independent from the rest of the framework as possible. So if you don't like a specific tool, you don’t need to use it.






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