Deep reinforcement learning
1-2 of 67 results

Deep Reinforcement Learning and GANs LiveLessons: Advanced Topics in Deep Learning

Machine Vision, GANs, and Deep Reinforcement Learning LiveLessons

Disney princesas - solapa média com 10 livros de histórias.

Foundations of Deep Reinforcement Learning: Theory and Practice in Python

Deep Reinforcement Learning Hands-On

This practical guide will teach you how deep learning (dl) can be used to solve complex real-world problems. key features explore deep reinforcement learning (rl), from the first principles to the latest algorithms evaluate high-profile rl methods, including value iteration, deep q-networks, policy gradients, trpo, ppo, ddpg, d4pg, evolution strategies and genetic algorithms keep up with the very latest industry developments, including ai-driven chatbots book descriptionrecent 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.

Foundations of Deep Reinforcement Learning

The contemporary introduction to deep reinforcement learning that combines theory and practice deep reinforcement learning (deep rl) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. in the past decade deep rl has achieved remarkable results on a range of problems, from single and multiplayer games?

Deep Learning and Reinforcement Learning with Tensorflow

Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? than this course is for you!

Python Reinforcement Learning, Deep Q-Learning and TRFL

Copo mimo com canudo 650 ml vermelho, preto e amarelo.

Deep Reinforcement Learning: Hands-on AI Tutorial in Python

In this course we learn the concepts and fundamentals of reinforcement learning, it's relation to artificial intelligence and machine learning, and how we can formulate a problem in the context of reinforcement learning and markov decision process. we cover different fundamental algorithms including q-learning, sarsa as well as deep q-learning.

Modern Reinforcement Learning: Deep Q Learning in PyTorch

In this complete deep reinforcement learning course you will learn a repeatable framework for reading and implementing deep reinforcement learning research papers. you will read the original papers that introduced the deep q learning, double deep qlearning, and dueling deep q learning algorithms.

2.0 (Deep Reinforcement Learning 2.0)

Sao paulo - um apelo a conversao.

Cutting-Edge AI: Deep Reinforcement Learning in Python

Caixa de luz 2 x 4 plástica amarela roma com 24.

Deep Reinforcement Learning 2.0

Welcome to deep reinforcement learning 2.0!

tensorflow for deep learning from linear regression to reinforcement learni

Par palhetas diant 24/18 pol ecosport g1 03 a 12.

deep reinforcement learning hands on apply modern rl methods with deep q ne

Alforge nylon 26 litros + afastador kawasaki ninja 300.

Lapan, Maxim: Deep Reinforcement Learning Hands-On

Erscheinungsdatum: 20.06.

Sahakjan, Artur: A Review of Recent Advancements in Deep Reinforcement Learning

Erscheinungsdatum: 03.07.

Deep Reinforcement Learning with Guaranteed Performance

Erscheinungsdatum: 20.11.

Deep Reinforcement Learning for Wireless Networks

Erscheinungsdatum: 29.01.

François-Lavet, Vincent: An Introduction to Deep Reinforcement Learning

Erscheinungsdatum: 31.03.

Lapan, Maxim: Deep Reinforcement Learning Hands-On - Second Edition

Erscheinungsdatum: 31.01.

Schurr, A: Deep Traffic Reinforcement Learning. Steuern eine

Erscheinungsdatum: 14.01.

Lapan:Deep Reinforcement Learning

Erscheinungsdatum: 07/2020, medium: taschenbuch, einband: kartoniert / broschiert, titel: deep reinforcement learning, titelzusatz: das umfassende praxis-handbuch. moderne algorithmen für chatbots, robotik, diskrete optimierung und web-automatisierung inkl.

Zai, Alexander: Einstieg in Deep Reinforcement Learning

Erscheinungsdatum: 12.10.

Graesser, Laura: Foundations of Deep Reinforcement Learning

Erscheinungsdatum: 01/2020, medium: taschenbuch, einband: kartoniert / broschiert, titel: foundations of deep reinforcement learning, titelzusatz: theory and practice in python, autor: graesser, laura // wah loon, keng, verlag: addison wesley, imprint: pearson professional, sprache: englisch, schlagworte: programmiersprachen // intelligenz // künstliche intelligenz // ki // ai // database // datenbank // data mining // edv, rubrik: programmiersprachen, seiten: 379, gewicht: 499 gr, verkäufer: averdo.

Blokdyk, Gerardus: Deep Reinforcement Learning A Complete Guide - 2019 Edition

Erscheinungsdatum: 18.03.

Blokdyk, Gerardus: Deep Reinforcement Learning Complete Self-Assessment Guide

Erscheinungsdatum: 09.09.

Deep Traffic Reinforcement Learning. Steuern eines Fahrzeugs durch eine simulierte Straßenumgebung mit dichtem Verkehr

Studienarbeit aus dem jahr 2019 im fachbereich informatik - wirtschaftsinformatik, note: 1,7, fom essen, hochschule für oekonomie &, management gemeinnützige gmbh, hochschulleitung essen früher fachhochschule, veranstaltung: big data &, data science, sprache: deutsch, abstract: in den folgenden abschnitten dieser arbeit sollen die themen rund um reinforcement learning und ein praxisbeispiel mit hilfe von reinforcement learning dargestellt werden. das praxisbeispiel bezieht sich auf ein simuliertes verkehrssystem, welches mit hilfe von reinforcement learning selbstständig lernt, ob und wann ein fahrzeug eigenständig überholen soll.

Deep Reinforcement Learning with Python - With PyTorch, TensorFlow and OpenAI Gym

Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics, healthcare, finance, and many more. this book covers deep reinforcement learning using deep-q learning and policy gradient models with coding exercise.

Deep Reinforcement Learning State of the art

Artificial intelligence has made big steps forward with reinforcement learning (rl) in the last century, and with the advent of deep learning (dl) in the 90s, especially, the breakthrough of convolutional networks in computer vision field. the adoption of dl neural networks in rl, in the first decade of the 21 century, led to an end-to-end framework allowing a great advance in human-level agents and autonomous systems, called deep reinforcement learning (drl).

Einstieg in Deep Reinforcement Learning - KI-Agenten mit Python und PyTorch programmieren. Inkl. E-Book

- grundlegende konzepte und terminologie- praktischer einsatz mit pytorch- projekte umsetzendieses buch zeigt ihnen, wie sie agenten programmieren, die basierend auf direktem feedback aus ihrer umgebung selbstständig lernen und sich dabei verbessern. sie werden netzwerke mit dem beliebten pytorch-deep-learning-framework aufbauen, um bestärkende lernalgorithmen zu erforschen.

Deep Reinforcement Learning - Das umfassende Praxis-Handbuch. Moderne Algorithmen für Chatbots, Robotik, diskrete Optimierung und Web-Automatisierung inkl. Multiagenten-Methoden

Alle wichtigen methoden und algorithmen praxisnah erläutert mit codebeispielen in python selbstständig lernende agenten programmieren für die steuerung von robotern, nlp in interaktiven spielen, chatbots und mehr deep q-networks, wertiteration, policy gradients, trust region policy optimization (trpo), genetische algorithmen, moderne explorationsverfahren u.v.

Deep Reinforcement Learning in Unity - With Unity ML Toolkit

Gain an in-depth overview of reinforcement learning for autonomous agents in game development with unity.this book starts with an introduction to state-based reinforcement learning algorithms involving markov models, bellman equations, and writing custom c# code with the aim of contrasting value and policy-based functions in reinforcement learning.

Steuerung eines autonomen Fahrzeugs durch Deep Reinforcement Learning

Andreas folkers stellt in diesem buch einen regelalgorithmus zur steuerung eines autonomen fahrzeugs vor. um eine nichtlineare bewegungsdynamik bei gleichzeitig kurzer rechenzeit zu berücksichtigen, wird der regler als neuronales netz definiert und dessen parameter im vorfeld gelernt.

Deep Reinforcement Learning Hands-On - Second Edition

Deep reinforcement learning hands-on - second edition.

A Review of Recent Advancements in Deep Reinforcement Learning

A review of recent advancements in deep reinforcement learning.

Deep Reinforcement Learning A Complete Guide - 2019 Edition

Deep reinforcement learning a complete guide - 2019 edition.

Deep Reinforcement Learning with Python - Second Edition

Deep reinforcement learning with python - second edition.

Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more