Simple Reinforcement learning tutorials

Minimal and Clean Reinforcement Learning Examples

An elegant, flexible, and superfast PyTorch deep reinforcement learning platform. (For previewed features, please checkout the "dev" branch)

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

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

Deep Reinforcement Learning For Sequence to Sequence Models

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

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

Implementations of Reinforcement Learning Models in Tensorflow

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

DEEp Reinforcement learning framework

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..

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

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

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

Trust Region Policy Optimization with TensorFlow and OpenAI Gym

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

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

Structural implementation of RL key algorithms

Multiple implementations for abstractive text summurization , using google colab

[파이썬과 케라스로 배우는 강화학습] 예제

Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

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

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

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

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

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

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

Highly Modular and Scalable Reinforcement Learning

강화학습에 대한 기본적인 알고리즘 구현