781 Open source projects that are alternatives of or similar to Tensorflow Reinforce

Minimal and Clean Reinforcement Learning Examples

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

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

No description or website provided.

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..

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

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

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

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Highly Modular and Scalable Reinforcement Learning

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

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

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

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

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.

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

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

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

Deep Reinforcement Learning For Sequence to Sequence Models

RAD: Reinforcement Learning with Augmented Data

DrQ: Data regularized Q

StarCraft II - pysc2 Deep Reinforcement Learning Examples

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

lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.

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

Reinforcement Learning Algorithms

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

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

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

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

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

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

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

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

A3C LSTM Atari with Pytorch plus A3G design

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

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

PyTorch implementation of Soft Actor-Critic (SAC)

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

Deep Reinforcement Learning with pytorch & visdom

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

A reinforcement learning package for Julia

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).

TensorFlow A2C to solve Acrobot, with synchronized parallel environments

Deep Reinforcement Learning in Autonomous Driving: the A3C algorithm used to make a car learn to drive in TORCS; Python 3.5, Tensorflow, tensorboard, numpy, gym-torcs, ubuntu, latex

A Universal Deep Reinforcement Learning Framework

A course on Deep Reinforcement Learning in Computer Vision. Visit Website:

DEEp Reinforcement learning framework

With underflow, create trafic light clusters that interact together to regulate circulation

PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.

A curated list of Monte Carlo tree search papers with implementations.

JAX implementations of core Deep RL algorithms

Lightweight deep RL Libraray for continuous control.

Deep Reinforcement Learning Algorithms implemented with Tensorflow 2.3

A PyTorch library for building deep reinforcement learning agents.

Repo for the Deep Reinforcement Learning Nanodegree program

Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.

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)

NeurIPS 2019: DQN(λ) = Deep Q-Network + λ-returns.