Displaying 1 to 15 from 15 results

stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms

  •    Python

Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post.

wger - Self hosted FLOSS fitness/workout, nutrition and weight tracker written with Django

  •    Python

wger (ˈvɛɡɐ) Workout Manager is a free, open source web application that help you manage your personal workouts, weight and diet plans and can also be used as a simple gym management utility. It offers a REST API as well, for easy integration with other projects and tools. These are the basic steps to install and run the application locally on a Linux system. There are more detailed instructions, other deployment options as well as an administration guide available at https://wger.readthedocs.io or locally in your code repository in the docs folder.




squadgym - Environment that can be used to evaluate reasoning capabilities of artificial agents

  •    Python

Recently, the Question Answering dataset Stanford Question Answering Dataset (SQuAD) has gained a lot of attention from practitioners and researchers due its appealing properties for evaluating the capabilities of agents able to answer open domain questions. In this dataset, given a reference context and a question, the agent should be able to generate an answer which may be composed by multiple tokens which are present in the given context. Due to its high quality, it represents a relevant benchmark for intelligent agents able to grasp, from a given context, relevant evidences that are required to generate the answer. The SQuAD dataset contains questions extracted from Wikipedia and related to specific entities. If the agent is able to extract from the related context text the sequence of tokens which compose the answer we may legitimately state that the system demonstrate sound reasoning capabilities. Obviously, the system should be able to generate an answer without exploiting supplementary features associated to the question or to the context but it should be able to "read" from the context text the correct answer.

NAF-tensorflow - "Continuous Deep Q-Learning with Model-based Acceleration" in TensorFlow

  •    Python

TensorFlow implementation of Continuous Deep q-Learning with Model-based Acceleration. Training details of Pendulum-v0 with different hyperparameters.

laravel-gymie - Gym & Club Management System https://gymie.in

  •    Javascript

Currently, we are in the process of polishing the code to be ready for general use. Check issues & milestone to know more about upcoming changes, features and improvements.


pytorch-A3C - Simple A3C implementation with pytorch + multiprocessing

  •    Python

This is a toy example of using multiprocessing in Python to asynchronously train a neural network to play discrete action CartPole and continuous action Pendulum games. The asynchronous algorithm I used is called Asynchronous Advantage Actor-Critic or A3C. I believe it would be the simplest toy implementation you can find at the moment (2018-01).

robotics-rl-srl - S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics

  •    Python

This repository was made to evaluate State Representation Learning methods using Reinforcement Learning. It integrates (automatic logging, plotting, saving, loading of trained agent) various RL algorithms (PPO, A2C, ARS, ACKTR, DDPG, DQN, ACER, CMA-ES, SAC, TRPO) along with different SRL methods (see SRL Repo) in an efficient way (1 Million steps in 1 Hour with 8-core cpu and 1 Titan X GPU). We also release customizable Gym environments for working with simulation (Kuka arm, Mobile Robot in PyBullet, running at 250 FPS on a 8-core machine) and real robots (Baxter Robot, Robobo with ROS).

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.

reactgym - Workout-Tracking app built in ES6 using react, flux and ImmutableJS

  •    Javascript

Workout-Tracking app built in ES6 using react, flux and ImmutableJS. It runs on the Web as well as on Android/iOS using Phonegap. I wrote this app to get more familiar with react development as a whole, including extensive unit testing using Jest. It is written completely in ES6 and uses Immutable datastructures for all application state. The application follows the flux pattern and has all the cool gadgets for productive development like webpack, eslint and react-hot-loader.

flutter - Flutter fitness/workout app for wger

  •    Dart

wger is a free, open source flutter application that manages and tracks/logs your exercises and personal workouts, weight and diet plans. This is the mobile app written with Flutter, it talks via REST with the main server. Alternatively, you can use one of our test servers, just ask us for access.

jaxrl - Jax (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces

  •    Jupyter

The goal of this repository is to provide simple and clean implementations to build research on top of. Please do not use this repository for baseline results and use the original implementations instead (SAC, AWAC, DrQ). If you want to run this code on GPU, please follow instructions from the official repository.

safe-control-gym - PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning

  •    Python

Physics-based CartPole and Quadrotor Gym environments (using PyBullet) with symbolic a priori dynamics (using CasADi) for learning-based control, and model-free and model-based reinforcement learning (RL). These environments include (and evaluate) symbolic safety constraints and implement input, parameter, and dynamics disturbances to test the robustness and generalizability of control approaches.






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