[Télécharger] TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python de Kaushik Balakrishnan En Ligne
Télécharger TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python de Kaushik Balakrishnan livre En ligne
Télécharger "TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python" de Kaushik Balakrishnan En Ligne
Auteur : Kaushik Balakrishnan
Catégorie : Livres anglais et étrangers,Computers & Internet,Computer Science
Broché : * pages
Éditeur : *
Langue : Français, Anglais
Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key Features Explore efficient Reinforcement Learning algorithms and code them using TensorFlow and Python Train Reinforcement Learning agents for problems, ranging from computer games to autonomous driving. Formulate and devise selective algorithms and techniques in your applications in no time. Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learn Understand the theory and concepts behind modern Reinforcement Learning algorithms Code state-of-the-art Reinforcement Learning algorithms with discrete or continuous actions Develop Reinforcement Learning algorithms and apply them to training agents to play computer games Explore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlow Use A3C to play CartPole and LunarLander Train an agent to drive a car autonomously in a simulator Who this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.Table of Contents Up and Running with Reinforcement Learning Temporal Difference, SARSA, and Q-Learning Deep Q-Network Double DQN, Dueling Architectures, and Rainbow Deep Deterministic Policy Gradient Asynchronous Methods - A3C and A2C Trust Region Policy Optimization and Proximal Policy Optimization Deep RL Applied to Autonomous Driving
Télécharger TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with training and deploying intelligent, self-learning agents using Python de Kaushik Balakrishnan Pdf Epub
TensorFlow 2 quickstart for beginners / TensorFlow Core ~ In Colab, connect to a Python runtime: At the top-right of the menu bar, select CONNECT. Run all the notebook code cells: Select Runtime > Run all. Download and install TensorFlow 2. Import TensorFlow into your program: Note: Upgrade pip to install the TensorFlow 2 package. See the install guide for details. import tensorflow as tf
Install TensorFlow 2 ~ No install necessary—run the TensorFlow tutorials directly in the browser with Colaboratory, a Google research project created to help disseminate machine learning education and research.It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. Read the blog post.
Tutorials / TensorFlow Core ~ The best place to start is with the user-friendly Keras sequential API. Build models by plugging together building blocks. After these tutorials, read the Keras guide. Beginner quickstart This "Hello, World!" notebook shows the Keras Sequential API and model.fit. Keras basics This notebook collection demonstrates basic machine learning tasks using Keras. Load data These tutorials use tf.data .
GitHub - tensorflow/agents: TF-Agents is a library for ~ @misc{TFAgents, title = {{TF-Agents}: A library for Reinforcement Learning in TensorFlow}, author = {Sergio Guadarrama and Anoop Korattikara and Oscar Ramirez and Pablo Castro and Ethan Holly and Sam Fishman and Ke Wang and Ekaterina Gonina and Neal Wu and Efi Kokiopoulou and Luciano Sbaiz and Jamie Smith and Gábor Bartók and Jesse Berent and Chris Harris and Vincent Vanhoucke and Eugene .
TensorFlow ~ TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Introduction to TensorFlow ~ Deploy a production-ready ML pipeline for training and inference using TensorFlow Extended (TFX). Learn more. Swift for TensorFlow Integrate directly with Swift for TensorFlow, the next generation platform for deep learning and differentiable programming. Learn more. TensorFlow ecosystem TensorFlow provides a collection of workflows to develop and train models using Python, JavaScript, or .
Reinforcement Learning (DQN) Tutorial — PyTorch Tutorials ~ Reinforcement Learning (DQN) Tutorial¶ Author: Adam Paszke. This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v0 task from the OpenAI Gym. Task. The agent has to decide between two actions - moving the cart left or right - so that the pole attached to it stays upright.
Gym: A toolkit for developing and comparing reinforcement ~ Open source interface to reinforcement learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks.. import gym env = gym.make("CartPole-v1") observation = env.reset() for _ in range(1000): env.render() action = env.action_space.sample() # your agent here (this takes random actions) observation, reward, done, info = env.step(action) if done: observation = env .
GitHub - Unity-Technologies/ml-agents: Unity Machine ~ Unity ML-Agents Toolkit (latest release) (all releases)The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. We provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and .
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Projects - OpenAI ~ Environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. Learn more. Spinning Up in Deep RL. An educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Visit Learn more. Emergent Tool Use from Multi-Agent Interaction. We’ve observed agents discovering .
Keras: the Python deep learning API ~ Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles. An accessible superpower. Because of its ease-of-use and focus on user experience, Keras is the deep learning solution of choice for many university courses.
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