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▼a 1789139708 |
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▼a 9781789139709
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| 100 | 1 |
▼a Lonza, Andrea. |
| 245 | 10 |
▼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 |
| 337 | |
▼a computer
▼b c
▼2 rdamedia |
| 338 | |
▼a online resource
▼b cr
▼2 rdacarrier |
| 500 | |
▼a Implementing REINFORCE with baseline |
| 505 | 0 |
▼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 | 8 |
▼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 | 8 |
▼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 | 8 |
▼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 | 8 |
▼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 | 0 |
▼a Print version record. |
| 590 | |
▼a Added to collection customer.56279.3 |
| 650 | 0 |
▼a Computer algorithms. |
| 650 | 0 |
▼a Python (Computer program language) |
| 650 | 7 |
▼a Computer algorithms.
▼2 fast
▼0 (OCoLC)fst00872010 |
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▼a Python (Computer program language)
▼2 fast
▼0 (OCoLC)fst01084736 |
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▼a Electronic books. |
| 776 | 08 |
▼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, 짤2019
▼z 9781789131116 |
| 856 | 40 |
▼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
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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 관리자 |
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