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Hands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
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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.
https://www.amazon.com/gp/product/1788836529?pf_rd_p=d1f45e03-8b73-4c9a-9beb-4819111bef9a&pf_rd_r=N714661D728RDMQYP5N2https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python
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Reinforcement-learning-with-tensorflow - Simple Reinforcement learning tutorials
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years. If you speak Chinese, visit 莫烦 Python or my Youtube channel for more.
reinforcement-learning tutorial q-learning sarsa sarsa-lambda deep-q-network a3c ddpg policy-gradient dqn double-dqn prioritized-replay dueling-dqn deep-deterministic-policy-gradient asynchronous-advantage-actor-critic actor-critic tensorflow-tutorials proximal-policy-optimization ppo machine-learningAtari - Persistent advantage learning dueling double DQN for the Arcade Learning Environment
Work In Progress: Crossed out items have been partially implemented. Prioritised experience replay [1] persistent advantage learning [2] bootstrapped [3] dueling [4] double [5] deep recurrent [6] Q-network [7] for the Arcade Learning Environment [8] (and custom environments). Or PERPALB(triple-D)RQN for short...
deep-reinforcement-learning deep-learningDeep_reinforcement_learning_Course - Implementations from the free course Deep Reinforcement Learning with Tensorflow
Deep Reinforcement Learning Course is a free series of blog posts and videos 🆕 about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow. 📜The articles explain the concept from the big picture to the mathematical details behind it.
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I made minor tweaks to this repository such as load and save functions for convenience. I also made the memory a deque instead of just a list. This is in order to limit the maximum number of elements in the memory.
deep-reinforcement-learning deep-q-network dqn reinforcement-learning deep-learning ddqnSLM-Lab - Modular Deep Reinforcement Learning framework in PyTorch.
Modular Deep Reinforcement Learning framework in PyTorch. A multitask agent solving both OpenAI Cartpole-v0 and Unity Ball2D.
reinforcement-learning pytorch openai-gym framework research dqn artificial-intelligence policy-gradient actor-critic ppo a3c deep-rlchainerrl - ChainerRL is a deep reinforcement learning library built on top of Chainer.
ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. ChainerRL is tested with Python 2.7+ and 3.5.1+. For other requirements, see requirements.txt.
chainer reinforcement-learning deep-learning machine-learning dqn actor-criticdeep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program
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.
deep-reinforcement-learning reinforcement-learning reinforcement-learning-algorithms neural-networks pytorch pytorch-rl ddpg dqn ppo dynamic-programming cross-entropy hill-climbing ml-agents openai-gym-solutions openai-gym rl-algorithmsDeepLearningFlappyBird - Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).
This project follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning [2] and shows that this learning algorithm can be further generalized to the notorious Flappy Bird. It is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards.
deep-learning deep-reinforcement-learning gameRLSeq2Seq - Deep Reinforcement Learning For Sequence to Sequence Models
NOTE: THE CODE IS UNDER DEVELOPMENT, PLEASE ALWAYS PULL THE LATEST VERSION FROM HERE. In recent years, sequence-to-sequence (seq2seq) models are used in a variety of tasks from machine translation, headline generation, text summarization, speech to text, to image caption generation. The underlying framework of all these models are usually a deep neural network which contains an encoder and decoder. The encoder processes the input data and a decoder receives the output of the encoder and generates the final output. Although simply using an encoder/decoder model would, most of the time, produce better result than traditional methods on the above-mentioned tasks, researchers proposed additional improvements over these sequence to sequence models, like using an attention-based model over the input, pointer-generation models, and self-attention models. However, all these seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently a completely fresh point of view emerged in solving these two problems in seq2seq models by using methods in Reinforcement Learning (RL). In these new researches, we try to look at the seq2seq problems from the RL point of view and we try to come up with a formulation that could combine the power of RL methods in decision-making and sequence to sequence models in remembering long memories. In this paper, we will summarize some of the most recent frameworks that combines concepts from RL world to the deep neural network area and explain how these two areas could benefit from each other in solving complex seq2seq tasks. In the end, we will provide insights on some of the problems of the current existing models and how we can improve them with better RL models. We also provide the source code for implementing most of the models that will be discussed in this paper on the complex task of abstractive text summarization.
reinforcement-learning actor-critic policy-gradient abstractive-text-summarization pointer-generator nlpasync-rl - Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"
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.
dissecting-reinforcement-learning - Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
This repository contains the code and pdf of a series of blog post called "dissecting reinforcement learning" which I published on my blog mpatacchiola.io/blog. Moreover there are links to resources that can be useful for a reinforcement learning practitioner. If you have some good references which may be of interest please send me a pull request and I will integrate them in the README. The source code is contained in src with the name of the subfolders following the post number. In pdf there are the A3 documents of each post for offline reading. In images there are the raw svg file containing the images used in each post.
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Deep Reinforcement Learning for the JVM
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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.
reinforcement-learning tensorflow deep-reinforcement-learning deep-q-networkdeep-rl-tensorflow - TensorFlow implementation of Deep Reinforcement Learning papers
Result of Corridor-v5 in [4] for DQN (purple), DDQN (red), Dueling DQN (green), Dueling DDQN (blue).
tensorflow deep-reinforcement-learning dqntensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists
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.
tensorlayer deep-learning tensorflow machine-learning data-science neural-network reinforcement-learning artificial-intelligence gan a3c tensorflow-tutorials dqn object-detection chatbot tensorflow-tutorial imagenet googleDeepMind-Atari-Deep-Q-Learner - The original code from the DeepMind article + my tweaks
This repository hosts the original code published along with the article in Nature and my experiments (if any) with it. This project contains the source code of DQN 3.0, a Lua-based deep reinforcement learning architecture, necessary to reproduce the experiments described in the paper "Human-level control through deep reinforcement learning", Nature 518, 529–533 (26 February 2015) doi:10.1038/nature14236.
async-rl - Replicating "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv
(2017/02/25) Now the A3C implementation in this repository has been ported into ChainerRL, a Chainer-based deep reinforcement learning library, with some enhancement such as support for continuous actions by Gaussian policies and n-step Q-learning, so I recommend using it instead of this repository. This is a repository where I attempt to reproduce the results of Asynchronous Methods for Deep Reinforcement Learning. Currently I have only replicated A3C FF/LSTM for Atari.
openai_lab - An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras
NOTICE: Please use the next version, SLM-Lab. An experimentation framework for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras.
keras tensorflow openai experiment policy-gradient actor-critic ddpg deep-reinforcement-learning reinforcement-learning gym lab reinforcement learningDeep-Reinforcement-Learning-Hands-On - Hands-on Deep Reinforcement Learning, published by Packt
This is the code repository for Deep Reinforcement Learning Hands-On, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace.
DRL-FlappyBird - Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow)
The code of DQN is only 160 lines long. Since the DQN code is a unique class, you can use it to play other games.
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