Hands-on reinforcement learning with python : master reinforcement learning and deep reinforcement learning by building intelligent app

Hands-on reinforcement learning with python master reinforcement learning and deep reinforcement learning by building intelligent app

A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python About This Book Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore various state-of-the-art arc...

Descripción completa

Detalles Bibliográficos
Otros Autores: Ravichandiran, Sudharsan, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, London ; Mumbai : Packt 2018.
Edición:1st edition
Materias:
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630398506719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Dedication
  • Packt Upsell
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Introduction to Reinforcement Learning
  • What is RL?
  • RL algorithm
  • How RL differs from other ML paradigms
  • Elements of RL
  • Agent
  • Policy function
  • Value function
  • Model
  • Agent environment interface
  • Types of RL environment
  • Deterministic environment
  • Stochastic environment
  • Fully observable environment
  • Partially observable environment
  • Discrete environment
  • Continuous environment
  • Episodic and non-episodic environment
  • Single and multi-agent environment
  • RL platforms
  • OpenAI Gym and Universe
  • DeepMind Lab
  • RL-Glue
  • Project Malmo
  • ViZDoom
  • Applications of RL
  • Education
  • Medicine and healthcare
  • Manufacturing
  • Inventory management
  • Finance
  • Natural Language Processing and Computer Vision
  • Summary
  • Questions
  • Further reading
  • Chapter 2: Getting Started with OpenAI and TensorFlow
  • Setting up your machine
  • Installing Anaconda
  • Installing Docker
  • Installing OpenAI Gym and Universe
  • Common error fixes
  • OpenAI Gym
  • Basic simulations
  • Training a robot to walk
  • OpenAI Universe
  • Building a video game bot
  • TensorFlow
  • Variables, constants, and placeholders
  • Variables
  • Constants
  • Placeholders
  • Computation graph
  • Sessions
  • TensorBoard
  • Adding scope
  • Summary
  • Questions
  • Further reading
  • Chapter 3: The Markov Decision Process and Dynamic Programming
  • The Markov chain and Markov process
  • Markov Decision Process
  • Rewards and returns
  • Episodic and continuous tasks
  • Discount factor
  • The policy function
  • State value function
  • State-action value function (Q function)
  • The Bellman equation and optimality
  • Deriving the Bellman equation for value and Q functions
  • Solving the Bellman equation.
  • Dynamic programming
  • Value iteration
  • Policy iteration
  • Solving the frozen lake problem
  • Value iteration
  • Policy iteration
  • Summary
  • Questions
  • Further reading
  • Chapter 4: Gaming with Monte Carlo Methods
  • Monte Carlo methods
  • Estimating the value of pi using Monte Carlo
  • Monte Carlo prediction
  • First visit Monte Carlo
  • Every visit Monte Carlo
  • Let's play Blackjack with Monte Carlo
  • Monte Carlo control
  • Monte Carlo exploration starts
  • On-policy Monte Carlo control
  • Off-policy Monte Carlo control
  • Summary
  • Questions
  • Further reading
  • Chapter 5: Temporal Difference Learning
  • TD learning
  • TD prediction
  • TD control
  • Q learning
  • Solving the taxi problem using Q learning
  • SARSA
  • Solving the taxi problem using SARSA
  • The difference between Q learning and SARSA
  • Summary
  • Questions
  • Further reading
  • Chapter 6: Multi-Armed Bandit Problem
  • The MAB problem
  • The epsilon-greedy policy
  • The softmax exploration algorithm
  • The upper confidence bound algorithm
  • The Thompson sampling algorithm
  • Applications of MAB
  • Identifying the right advertisement banner using MAB
  • Contextual bandits
  • Summary
  • Questions
  • Further reading
  • Chapter 7: Deep Learning Fundamentals
  • Artificial neurons
  • ANNs
  • Input layer
  • Hidden layer
  • Output layer
  • Activation functions
  • Deep diving into ANN
  • Gradient descent
  • Neural networks in TensorFlow
  • RNN
  • Backpropagation through time
  • Long Short-Term Memory RNN
  • Generating song lyrics using LSTM RNN
  • Convolutional neural networks
  • Convolutional layer
  • Pooling layer
  • Fully connected layer
  • CNN architecture
  • Classifying fashion products using CNN
  • Summary
  • Questions
  • Further reading
  • Chapter 8: Atari Games with Deep Q Network
  • What is a Deep Q Network?
  • Architecture of DQN
  • Convolutional network.
  • Experience replay
  • Target network
  • Clipping rewards
  • Understanding the algorithm
  • Building an agent to play Atari games
  • Double DQN
  • Prioritized experience replay
  • Dueling network architecture
  • Summary
  • Questions
  • Further reading
  • Chapter 9: Playing Doom with a Deep Recurrent Q Network
  • DRQN
  • Architecture of DRQN
  • Training an agent to play Doom
  • Basic Doom game
  • Doom with DRQN
  • DARQN
  • Architecture of DARQN
  • Summary
  • Questions
  • Further reading
  • Chapter 10: The Asynchronous Advantage Actor Critic Network
  • The Asynchronous Advantage Actor Critic
  • The three As
  • The architecture of A3C
  • How A3C works
  • Driving up a mountain with A3C
  • Visualization in TensorBoard
  • Summary
  • Questions
  • Further reading
  • Chapter 11: Policy Gradients and Optimization
  • Policy gradient
  • Lunar Lander using policy gradients
  • Deep deterministic policy gradient
  • Swinging a pendulum
  • Trust Region Policy Optimization
  • Proximal Policy Optimization
  • Summary
  • Questions
  • Further reading
  • Chapter 12: Capstone Project - Car Racing Using DQN
  • Environment wrapper functions
  • Dueling network
  • Replay memory
  • Training the network
  • Car racing
  • Summary
  • Questions
  • Further reading
  • Chapter 13: Recent Advancements and Next Steps
  • Imagination augmented agents
  • Learning from human preference
  • Deep Q learning from demonstrations
  • Hindsight experience replay
  • Hierarchical reinforcement learning
  • MAXQ Value Function Decomposition
  • Inverse reinforcement learning
  • Summary
  • Questions
  • Further reading
  • Assessments
  • Other Books You May Enjoy
  • Index.

Ejemplares similares