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deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program
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.
deep-reinforcement-learning reinforcement-learning reinforcement-learning-algorithms neural-networks pytorch pytorch-rl ddpg dqn ppo dynamic-programming cross-entropy hill-climbing ml-agents openai-gym-solutions openai-gym rl-algorithmsstable-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 gsderex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
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.
machine-learning reinforcement-learning robot robotics tensorflow openai-gym python3 artificial-intelligence inverse-kinematics openai reinforcement-learning-algorithms legged-robots quadruped robotic-arm openai-gym-environments pybullet gym-environment quadruped-robot-gaits quadruped-robot spotmicroReinforcement-Learning - 🤖 Implements of Reinforcement Learning algorithms.
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.
reinforcement-learning reinforcement-learning-algorithms papers dqn ddpg deep-reinforcement-learning deep-learning-algorithmsgoNEAT - The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to grow and teach Artificial Neural Networks without back propagation
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).
artificial-neural-networks neuroevolution neat augmenting-topologies unsupervised-learning unsupervised-machine-learning neural-network reinforcement-learning-algorithms reinforcement-learninggoNEAT_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
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).
neuroevolution neat novelty-search artificial-neural-networks augmenting-topologies unsupervised-learning unsupervised-machine-learning unsupervised-learning-algorithms reinforcement-learning-algorithms modular-ai explainable-ai explainable-artificial-intelligencePOMDPs
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.
pomdps markov-decision-processes julia artificial-intelligence control-systems reinforcement-learning reinforcement-learning-algorithms mdpstorchrl - Highly Modular and Scalable Reinforcement Learning
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.
machine-learning reinforcement-learning reinforcement-learning-algorithms pytorch deep-learning deep-reinforcement-learning python3 dqn policy-gradientagentmodels.org - Modeling agents with probabilistic programs
Modeling agents with probabilistic programs
webppl probabilistic-programming probabilistic-programs modeling-agents mdp pomdp reinforcement-learning reinforcement-learning-algorithmsking-pong - Deep Reinforcement Learning Pong Agent, King Pong, he's the best
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.
deep-reinforcement-learning deep-learning deep-q-network dqn q-learning agent machine-learning king-pong percept reinforcement-learning reinforcement-learning-algorithmsl2rpn-baselines - L2RPN Baselines a repository to host baselines for l2rpn competitions.
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).
reinforcement-learning-algorithms baseline powergrid-operation grid2opcoax - Modular framework for Reinforcement Learning in 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.
reinforcement-learning reinforcement-learning-algorithms reinforcement-learning-agent
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