Displaying 1 to 13 from 13 results

deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

  •    Jupyter

This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

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.

rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)

  •    Python

The goal of this project is to train an open-source 3D printed quadruped robot exploring Reinforcement Learning and OpenAI Gym. The aim is to let the robot learns domestic and generic tasks in the simulations and then successfully transfer the knowledge (Control Policies) on the real robot without any other manual tuning. This project is mostly inspired by the incredible works done by Boston Dynamics.

Reinforcement-Learning - 🤖 Implements of Reinforcement Learning algorithms.

  •    Python

This repo is implements of Reinforcement Learning Algorithms, implementing as learning, some of them are even another version of some tutorial. Any contributions are welcomed. Deep Deterministic Policy Gradient (DDPG) Implement of DDPG.




goNEAT - The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to grow and teach Artificial Neural Networks without back propagation

  •    Go

This repository provides implementation of NeuroEvolution of Augmenting Topologies (NEAT) method written in Go language. The Neuroevolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuroevolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).

goNEAT_NS - This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

  •    Go

This repository provides implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization implemented in GoLang. The Neuro-Evolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuro-Evolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).

POMDPs

  •    Julia

This package provides a core interface for working with Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). For examples, please see the Gallery. There are multiple interfaces for expressing and interacting with (PO)MDPs: When the explicit interface is used, the transition and observation probabilities are explicitly defined using api functions or tables; when the generative interface is used, only a single step simulator (e.g. (s', o, r) = G(s,a)) needs to be defined.

torchrl - Highly Modular and Scalable Reinforcement Learning

  •    Python

TorchRL provides highly modular and extensible approach to experimenting with Reinforcement Learning. It allows for a registry based approach to running experiments, allows easy checkpointing, and updating hyper parameter sets. All this is accessible via a programmatic interface as well as a friendly CLI. Install from source for the latest changes that have not been published to PyPI.


king-pong - Deep Reinforcement Learning Pong Agent, King Pong, he's the best

  •    Python

In this repository, you have an agent that plays the game of pong. Make no mistake though, this is not a normal player. King (the agent) has learned to play the game of pong all by himself, by looking at the screen just like you would. Now, as you can imagine, there are a lot of cutting edge technologies being mixed into this project. First, we have Computer Vision to be able to receive the percepts from the screen. Next, we have Reinforcement Learning which is part of Machine Learning, but it is not classification, nor regression, or clustering. Reinforcement Learning is inspired by the study of animal behavior. In specific, how animals react to pain, reward signals through time. King wants to win, that's why he learns to do what he does.

l2rpn-baselines - L2RPN Baselines a repository to host baselines for l2rpn competitions.

  •    Python

We welcome contributions: see the contribute guide for details. Say you want to know how you compared with the "DoubleDuelingDQN" baseline implementation in this repository (for the sake of this example).

coax - Modular framework for Reinforcement Learning in python

  •    Python

If you do require CUDA support, please check out the Installation Guide. Have a look at the Getting Started page to train your first RL agent.






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