814 Open source projects that are alternatives of or similar to Reinforcement Learning

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..

强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".

Implementations of Reinforcement Learning Models in Tensorflow

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.

Deep Reinforcement Learning with pytorch & visdom

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

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Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)

A3C LSTM Atari with Pytorch plus A3G design

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

This repository contains model-free deep reinforcement learning algorithms implemented in Pytorch

Scalable, event-driven, deep-learning-friendly backtesting library

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

Machine Learning and having it Deep and Structured (MLDS) in 2018 spring

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO

Explorer is a PyTorch reinforcement learning framework for exploring new ideas.

Reinforcement learning tutorials

🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.

Highly Modular and Scalable Reinforcement Learning

This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are still in progress)

A PyTorch library for building deep reinforcement learning agents.

Python library for Reinforcement Learning.

Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog

PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments

Lightweight, efficient and stable implementations of deep reinforcement learning algorithms using PyTorch.

Tutorials for reinforcement learning in PyTorch and Gym by implementing a few of the popular algorithms. [IN PROGRESS]

Deep Reinforcement Learning For Sequence to Sequence Models

StarCraft II - pysc2 Deep Reinforcement Learning Examples

PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.

playing idealized trading games with deep reinforcement learning

A high-performance Atari A3C agent in 180 lines of PyTorch

Minimal Monte Carlo Policy Gradient (REINFORCE) Algorithm Implementation in Keras

Repo for the Deep Reinforcement Learning Nanodegree program

Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

🤖 Implements of Reinforcement Learning algorithms.

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

Reinforcement Learning Algorithms

Deep reinforcement learning using baidu PARL(maze,flappy bird and so on)

ChainerRL is a deep reinforcement learning library built on top of Chainer.

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

A Clearer and Simpler Synchronous Advantage Actor Critic (A2C) Implementation in TensorFlow

🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!

Trained A Convolutional Neural Network To Play 2048 using Deep-Reinforcement Learning

PyTorch implementation of Soft Actor-Critic (SAC)

Implementations of deep RL papers and random experimentation

PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)

Code for Hands On Intelligent Agents with OpenAI Gym book to get started and learn to build deep reinforcement learning agents using PyTorch

A library for ready-made reinforcement learning agents and reusable components for neat prototyping

Structural implementation of RL key algorithms