580 Open source projects that are alternatives of or similar to Rlseq2seq

Implementations of Reinforcement Learning Models in Tensorflow

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

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..

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

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

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

Minimal and Clean Reinforcement Learning Examples

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

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

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

A3C LSTM Atari with Pytorch plus A3G design

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

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

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

Highly Modular and Scalable Reinforcement Learning

강화학습에 대한 기본적인 알고리즘 구현

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

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

Reason8.ai PyTorch solution for NIPS RL 2017 challenge

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

Code for "Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner" in ICCV 2017

PyTorch implementation of Soft Actor-Critic (SAC)

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

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

Deep Reinforcement Learning Algorithms implemented with Tensorflow 2.3

Deep Reinforcement Learning with pytorch & visdom

DrQ: Data regularized Q

Reaver: Modular Deep Reinforcement Learning Framework. Focused on StarCraft II. Supports Gym, Atari, and MuJoCo.

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

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

Reinforcement Learning Algorithms

Structural implementation of RL key algorithms

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

A resource for learning about deep learning techniques from regression to LSTM and Reinforcement Learning using financial data and the fitness functions of algorithmic trading

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

[파이썬과 케라스로 배우는 강화학습] 예제

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

Reinforcement learning tutorials

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

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

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).

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

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

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

Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout

Applied Deep Learning (2019 Spring) @ NTU

No description or website provided.

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

Multiple implementations for abstractive text summurization , using google colab

PyTorch C++ Reinforcement Learning

Reinforcement learning framework to accelerate research

Trust Region Policy Optimization with TensorFlow and OpenAI Gym

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

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

A simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)

A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators.

A C++ framework for MDPs and POMDPs with Python bindings

The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)