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coach - Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

  •    Python

Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless.

ELF - ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation

  •    C++

ELF is an Extensive, Lightweight, and Flexible platform for game research. We have used it to build our Go playing bot, ELF OpenGo, which achieved a 14-0 record versus four global top-30 players in April 2018. The final score is 20-0 (each professional Go players play 5 games). We have released our v0 models here.

seed_rl - SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference

  •    Python

This repository contains an implementation of distributed reinforcement learning agent where both training and inference are performed on the learner.

hra - Hybrid Reward Architecture

  •    Python

We strongly suggest to use Anaconda distribution. The rendered images will be saved in ./render directory by default.

spriteworld - Spriteworld: a flexible, configurable python-based reinforcement learning environment

  •    Python

Spriteworld is a python-based RL environment that consists of a 2-dimensional arena with simple shapes that can be moved freely. This environment was developed for the COBRA agent introduced in the paper "COBRA: Data-Efficient Model-Based RL through Unsupervised Object Discovery and Curiosity-Driven Exploration" (Watters et al., 2019). The motivation for the environment was to provide as much flexibility for procedurally generating multi-object scenes while retaining as simple an interface as possible. Spriteworld sprites come in a variety of shapes and can vary continuously in position, size, color, angle, and velocity. The environment has occlusion but no physics, so by default sprites pass beneath each other but do not collide or interact in any way. Interactions may be introduced through the action space, which can update all sprites each timestep. For example, the DiscreteEmbodied action space (see spriteworld/action_spaces.py) implements a rudimentary form of physics in which an agent's body sprite can adhere to and carry sprites underneath it.

gym-gazebo2 - gym-gazebo2 is a toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo

  •    Python

gym-gazebo2 is a toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo. Built as an extension of gym-gazebo, gym-gazebo2 has been redesigned with community feedback and adopts now a standalone architecture while mantaining the core concepts of previous work inspired originally by the OpenAI gym. A whitepaper regarding previous work of gym-gazebo is available at https://arxiv.org/abs/1608.05742.

ros2learn - ROS 2 enabled Machine Learning algorithms

  •    Python

This repository contains a number of ROS and ROS 2 enabled Artificial Intelligence (AI) and Reinforcement Learning (RL) algorithms that run in selected environments. Please refer to Install.md to install from sources.

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