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stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms
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.
machine-learning reinforcement-learning robotics pytorch toolbox openai gym reinforcement-learning-algorithms sde baselines stable-baselines sb3 gsdewger - Self hosted FLOSS fitness/workout, nutrition and weight tracker written with Django
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.
django fitness self-hosted gym workoutdeepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving
Note that we recorded the baseline dataset in sync mode which is much slower than async mode. Async mode probably is fine to record in, we just haven't got around to trying it out for v3.
competition control reinforcement-learning deep-learning simulation tensorflow deep-reinforcement-learning vision gym self-driving-car unreal-engine transfer-learning sensorimotoropenai_lab - An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras
NOTICE: Please use the next version, SLM-Lab. An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.
keras tensorflow openai experiment policy-gradient actor-critic ddpg deep-reinforcement-learning reinforcement-learning gym lab reinforcement learningrl-baselines3-zoo - A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included
RL Baselines3 Zoo is a training framework for Reinforcement Learning (RL), using Stable Baselines3. It provides scripts for training, evaluating agents, tuning hyperparameters, plotting results and recording videos.
reinforcement-learning robotics optimization lab openai gym hyperparameter-optimization rl sde hyperparameter-tuning hyperparameter-search pybullet stable-baselines pybullet-environments tuning-hyperparameterssquadgym - Environment that can be used to evaluate reasoning capabilities of artificial agents
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.
question-answering gym artificial-intelligenceNAF-tensorflow - "Continuous Deep Q-Learning with Model-based Acceleration" in TensorFlow
TensorFlow implementation of Continuous Deep q-Learning with Model-based Acceleration. Training details of Pendulum-v0 with different hyperparameters.
tensorflow gym continuous-rl reinforcement-learning deep-reinforcement-learning deep-learninglaravel-gymie - Gym & Club Management System https://gymie.in
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.
laravel laravel-application gym management-systempytorch-A3C - Simple A3C implementation with pytorch + multiprocessing
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).
pytorch a3c gym neural-network asynchronous-advantage-actor-critic multiprocessing toy-example actor-criticrobotics-rl-srl - S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics
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).
reinforcement-learning state representation-learning robotics kuka baxter-robot arm pytorch pybullet gym baselinesgym-gazebo2 - gym-gazebo2 is a toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo
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.
gym gazebo ros ros2 robotics reinforcement-learning deep-reinforcement-learning rl drlreactgym - Workout-Tracking app built in ES6 using react, flux and ImmutableJS
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.
react cordova gym app mobile-appflutter - Flutter fitness/workout app for wger
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.
dart fitness gym flutter workout wgerjaxrl - Jax (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces
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.
reinforcement-learning deep-learning deep-reinforcement-learning gym flax behavioral-cloning sac continuous-control deep-deterministic-policy-gradient jax soft-actor-critic offline-reinforcement-learning batch-reinforcement-learningsafe-control-gym - PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and reinforcement learning
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.
control reinforcement-learning quadcopter robotics symbolic gym cartpole safety quadrotor robustness pybullet casadi
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