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  ▼a  019591497
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  ▼a  1789139708

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  ▼a  9781789139709
  ▼q  (electronic bk.)

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  ▼a  2278656
  ▼b  (N$T)

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  ▼a  (OCoLC)1125107738

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  ▼a  D518365C-A419-4AF3-8B4C-AD04FD491FF8
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  ▼n  http://www.overdrive.com

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  ▼a  EBLCP
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  ▼a  QA76.9.A43

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  ▼a  005.1
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• 100

  ▼a  Lonza, Andrea.

• 245

  ▼a  Reinforcement Learning Algorithms with Python:
  ▼b  Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges /:
  ▼c  Andrea Lonza.

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing, Limited,
  ▼c  2019.

• 300

  ▼a  1 online resource (356 pages).

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

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  ▼a  computer
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  ▼2  rdamedia

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  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 500

  ▼a  Implementing REINFORCE with baseline

• 505

  ▼a  Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Algorithms and Environments; Chapter 1: The Landscape of Reinforcement Learning; An introduction to RL; Comparing RL and supervised learning; History of RL; Deep RL; Elements of RL; Policy; The value function; Reward; Model; Applications of RL; Games; Robotics and Industry 4.0; Machine learning; Economics and finance; Healthcare; Intelligent transportation systems; Energy optimization and smart grid; Summary; Questions; Further reading

• 505

  ▼a  Chapter 2: Implementing RL Cycle and OpenAI GymSetting up the environment; Installing OpenAI Gym; Installing Roboschool; OpenAI Gym and RL cycles; Developing an RL cycle; Getting used to spaces; Development of ML models using TensorFlow; Tensor; Constant; Placeholder; Variable; Creating a graph; Simple linear regression example; Introducing TensorBoard; Types of RL environments; Why different environments?; Open source environments; Summary; Questions; Further reading; Chapter 3: Solving Problems with Dynamic Programming; MDP; Policy; Return; Value functions; Bellman equation

• 505

  ▼a  Categorizing RL algorithmsModel-free algorithms; Value-based algorithms; Policy gradient algorithms; Actor-Critic algorithms; Hybrid algorithms; Model-based RL; Algorithm diversity; Dynamic programming; Policy evaluation and policy improvement; Policy iteration; Policy iteration applied to FrozenLake; Value iteration; Value iteration applied to FrozenLake; Summary; Questions; Further reading; Section 2: Model-Free RL Algorithms; Chapter 4: Q-Learning and SARSA Applications; Learning without a model; User experience; Policy evaluation; The exploration problem; Why explore?; How to explore

• 505

  ▼a  TD learningTD update; Policy improvement; Comparing Monte Carlo and TD; SARSA; The algorithm; Applying SARSA to Taxi-v2; Q-learning; Theory; The algorithm; Applying Q-learning to Taxi-v2; Comparing SARSA and Q-learning; Summary; Questions; Chapter 5: Deep Q-Network; Deep neural networks and Q-learning; Function approximation; Q-learning with neural networks; Deep Q-learning instabilities; DQN; The solution; Replay memory; The target network; The DQN algorithm; The loss function; Pseudocode; Model architecture; DQN applied to Pong; Atari games; Preprocessing; DQN implementation; DNNs

• 505

  ▼a  The experienced bufferThe computational graph and training loop; Results; DQN variations; Double DQN; DDQN implementation; Results; Dueling DQN; Dueling DQN implementation; Results; N-step DQN; Implementation; Results; Summary; Questions; Further reading; Chapter 6: Learning Stochastic and PG Optimization; Policy gradient methods; The gradient of the policy; Policy gradient theorem; Computing the gradient; The policy; On-policy PG; Understanding the REINFORCE algorithm; Implementing REINFORCE; Landing a spacecraft using REINFORCE; Analyzing the results; REINFORCE with baseline

• 520

  ▼a  With this book, you will understand the core concepts and techniques of reinforcement learning. You will take a look into each RL algorithm and will develop your own self-learning algorithms and models. You will optimize the algorithms for better precision, use high-speed actions and lower the risk of anomalies in your applications.

• 588

  ▼a  Print version record.

• 590

  ▼a  Added to collection customer.56279.3

• 650

  ▼a  Computer algorithms.

• 650

  ▼a  Python (Computer program language)

• 650

  ▼a  Computer algorithms.
  ▼2  fast
  ▼0  (OCoLC)fst00872010

• 650

  ▼a  Python (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01084736

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Lonza, Andrea.
  ▼t  Reinforcement Learning Algorithms with Python : Learn, Understand, and Develop Smart Algorithms for Addressing AI Challenges.
  ▼d  Birmingham : Packt Publishing, Limited, ©2019,
  ▼z  9781789131116

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  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2278656

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  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH36843042

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  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL5964771

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  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2278656

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  ▼a  강리원

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  ▼a  eBook

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