NAF-tensorflow - "Continuous Deep Q-Learning with Model-based Acceleration" in TensorFlow

  •        26

TensorFlow implementation of Continuous Deep q-Learning with Model-based Acceleration. Training details of Pendulum-v0 with different hyperparameters.

https://github.com/carpedm20/NAF-tensorflow

Tags
Implementation
License
Platform

   




Related Projects

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.

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.


Deep_reinforcement_learning_Course - Implementations from the free course Deep Reinforcement Learning with Tensorflow

  •    Jupyter

Deep Reinforcement Learning Course is a free series of blog posts and videos πŸ†• about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow. πŸ“œThe articles explain the concept from the big picture to the mathematical details behind it.

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.

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.

tensorforce - TensorForce: A TensorFlow library for applied reinforcement learning

  •    Python

TensorForce is an open source reinforcement learning library focused on providing clear APIs, readability and modularisation to deploy reinforcement learning solutions both in research and practice. TensorForce is built on top of TensorFlow and compatible with Python 2.7 and >3.5 and supports multiple state inputs and multi-dimensional actions to be compatible with any type of simulation or application environment. TensorForce also aims to move all reinforcement learning logic into the TensorFlow graph, including control flow. This both reduces dependencies on the host language (Python), thus enabling portable computation graphs that can be used in other languages and contexts, and improves performance.

tensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists

  •    Python

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.

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.

deep-rl-tensorflow - TensorFlow implementation of Deep Reinforcement Learning papers

  •    Python

Result of Corridor-v5 in [4] for DQN (purple), DDQN (red), Dueling DQN (green), Dueling DDQN (blue).

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.

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.

DeepRL-Agents - A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

  •    Jupyter

This repository contains a collection of reinforcement learning algorithms written in Tensorflow. The ipython notebook here were written to go along with a still-underway tutorial series I have been publishing on Medium. If you are new to reinforcement learning, I recommend reading the accompanying post for each algorithm.

alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4

  •    Python

A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play based reinforcement learning based on the AlphaGo Zero paper (Silver et al). It is designed to be easy to adopt for any two-player turn-based adversarial game and any deep learning framework of your choice. A sample implementation has been provided for the game of Othello in PyTorch, Keras and TensorFlow. An accompanying tutorial can be found here. We also have implementations for GoBang and TicTacToe. To use a game of your choice, subclass the classes in Game.py and NeuralNet.py and implement their functions. Example implementations for Othello can be found in othello/OthelloGame.py and othello/{pytorch,keras,tensorflow}/NNet.py.

spinningup - An educational resource to help anyone learn deep reinforcement learning.

  •    Python

This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). For the unfamiliar: reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning.