### EPG - Open-sourced code for Evolved Policy Gradients

•        11

The paper is located at https://arxiv.org/abs/1802.04821. A demonstration video can be found at https://youtu.be/-Z-ieH6w0LA. Houthooft, R., Chen, R. Y., Isola, P., Stadie, B. C., Wolski, F., Ho, J., Abbeel, P. (2018). Evolved Policy Gradients. arXiv preprint arXiv:1802.04821.

https://github.com/openai/EPG

 Tags machine-learning reinforcement-learning evolutionary-strategy continuous-control meta-learning Implementation Python License MIT Platform Windows Linux

## Evolutionary-Algorithm - Evolutionary Algorithm using Python

•    Python

In these tutorials, we will demonstrate and visualize algorithms like Genetic Algorithm, Evolution Strategy, NEAT etc. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.

## Hands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

•    Jupyter

Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more.

## keras-rl - Deep Reinforcement Learning for Keras.

•    Python

keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.

## deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

•    Jupyter

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.

## openai_lab - An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras

•    Python

NOTICE: Please use the next version, SLM-Lab. An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.

## pytorch-maml-rl - Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

•    Python

Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in Pytorch. This repository includes environments introduced in (Duan et al., 2016, Finn et al., 2017): multi-armed bandits, tabular MDPs, continuous control with MuJoCo, and 2D navigation task. Create a virtual environment, activate it and install the requirements in requirements.txt.

## coach - Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

•    Python

Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless.

## ELF - An End-To-End, Lightweight and Flexible Platform for Game Research

•    C++

ELF is an Extensive, Lightweight and Flexible platform for game research, in particular for real-time strategy (RTS) games. On the C++-side, ELF hosts multiple games in parallel with C++ threading. On the Python side, ELF returns one batch of game state at a time, making it very friendly for modern RL. In comparison, other platforms (e.g., OpenAI Gym) wraps one single game instance with one Python interface. This makes concurrent game execution a bit complicated, which is a requirement of many modern reinforcement learning algorithms. Besides, ELF now also provides a Python version for running concurrent game environments, by Python multiprocessing with ZeroMQ inter-process communication. See ./ex_elfpy.py for a simple example.

## handson-ml - A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow

•    Jupyter

First, you will need to install git, if you don't have it already. If you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.

## agents - Efficient Batched Reinforcement Learning in TensorFlow

•    Python

This project provides optimized infrastructure for reinforcement learning. It extends the OpenAI gym interface to multiple parallel environments and allows agents to be implemented in TensorFlow and perform batched computation. As a starting point, we provide BatchPPO, an optimized implementation of Proximal Policy Optimization. The algorithm to use is defined in the configuration and pendulum started here uses the included PPO implementation. Check out more pre-defined configurations in agents/scripts/configs.py.

## jetson-reinforcement - Deep reinforcement learning GPU libraries for NVIDIA Jetson with PyTorch, OpenAI Gym, and Gazebo robotics simulator

•    C++

In this tutorial, we'll be creating artificially intelligent agents that learn from interacting with their environment, gathering experience, and a system of rewards with deep reinforcement learning (deep RL). Using end-to-end neural networks that translate raw pixels into actions, RL-trained agents are capable of exhibiting intuitive behaviors and performing complex tasks. Ultimately, our aim will be to train reinforcement learning agents from virtual robotic simulation in 3D and transfer the agent to a real-world robot. Reinforcement learners choose the best action for the agent to perform based on environmental state (like camera inputs) and rewards that provide feedback to the agent about it's performance. Reinforcement learning can learn to behave optimally in it's environment given a policy, or task - like obtaining the reward.

## rl-portfolio-management - Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" https://arxiv

•    Jupyter

Attempting to replicate "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" by Jiang et. al. 2017 [1]. This paper trains an agent to choose a good portfolio of cryptocurrencies. It's reported that it can give 4-fold returns in 50 days and the paper seems to do all the right things so I wanted to see if I could acheive the same results.

## SLM-Lab - Modular Deep Reinforcement Learning framework in PyTorch.

•    Python

Modular Deep Reinforcement Learning framework in PyTorch. A multitask agent solving both OpenAI Cartpole-v0 and Unity Ball2D.

## gym-malware

•    Python

This is a malware manipulation environment for OpenAI's gym. OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This makes it possible to write agents that learn to manipulate PE files (e.g., malware) to achieve some objective (e.g., bypass AV) based on a reward provided by taking specific manipulation actions. Create an AI that learns through reinforcement learning which functionality-preserving transformations to make on a malware sample to break through / bypass machine learning static-analysis malware detection.

## basic_reinforcement_learning - An introduction series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials

•    Jupyter

This repository aims to provide an introduction series to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques. A quick background review of RL is available here.

## ml-agents - Unity Machine Learning Agents

•    CSharp

Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.

## DDPG - Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow

•    Python

It is still a problem to implement Batch Normalization on the critic network. However the actor network works well with Batch Normalization. Some Mujoco environments are still unsolved on OpenAI Gym.

## Meta-Learning-Papers - Meta Learning / Learning to Learn / One Shot Learning / Few Shot Learning

•

[1] Nicolas Schweighofer and Kenji Doya. Meta-learning in reinforcement learning. Neural Networks, 16(1):5–9, 2003. [2] Sepp Hochreiter, A Steven Younger, and Peter R Conwell. Learning to learn using gradient descent. In International Conference on Artificial Neural Networks, pages 87–94. Springer, 2001.

## deep-neuroevolution - Deep Neuroevolution

•    Python

Our code is based off of code from OpenAI, who we thank. The original code and related paper from OpenAI can be found here. The repo has been modified to run both ES and our algorithms, including our Deep Genetic Algorithm (DeepGA) locally and on AWS. The folder ./visual_inspector contains implementations of VINE, i.e., Visual Inspector for NeuroEvolution, an interactive data visualization tool for neuroevolution. Refer to README.md in that folder for further instructions on running and customizing your visualization. An article describing this visualization tool can be found here.

## async-rl - Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"

•    Python

This is a Tensorflow + Keras implementation of asyncronous 1-step Q learning as described in "Asynchronous Methods for Deep Reinforcement Learning". Since we're using multiple actor-learner threads to stabilize learning in place of experience replay (which is super memory intensive), this runs comfortably on a macbook w/ 4g of ram.

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