Reinforcement learning algorithms with Python: learn, understand, and develop smart algorithms for addressing AI challenges
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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt Publishing
October 2019
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| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | Implementing REINFORCE with baseline |
| Beschreibung: | vii, 351 Seiten Illustrationen, Diagramme |
| ISBN: | 9781789131116 |
Internformat
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| 245 | 1 | 0 | |a Reinforcement learning algorithms with Python |b learn, understand, and develop smart algorithms for addressing AI challenges |c Andrea Lonza |
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| 500 | |a Implementing REINFORCE with baseline | ||
| 505 | 8 | |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 | 3 | |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 | |
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Datensatz im Suchindex
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| author | Lonza, Andrea |
| author_facet | Lonza, Andrea |
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| author_sort | Lonza, Andrea |
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| building | Verbundindex |
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| contents | 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 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 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 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 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 |
| ctrlnum | (OCoLC)1164646408 (DE-599)BVBBV046768925 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| id | DE-604.BV046768925 |
| illustrated | Illustrated |
| index_date | 2024-07-03T14:46:12Z |
| indexdate | 2024-07-20T07:58:52Z |
| institution | BVB |
| isbn | 9781789131116 |
| language | English |
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| oclc_num | 1164646408 |
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| physical | vii, 351 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
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| publisher | Packt Publishing |
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| spelling | Lonza, Andrea Verfasser aut Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges Andrea Lonza Birmingham ; Mumbai Packt Publishing October 2019 vii, 351 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Implementing REINFORCE with baseline 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 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 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 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 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 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 Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Operante Konditionierung (DE-588)4172613-3 gnd rswk-swf Computer algorithms Python (Computer program language) Electronic books Python Programmiersprache (DE-588)4434275-5 s Operante Konditionierung (DE-588)4172613-3 s DE-604 Erscheint auch als Online-Ausgabe 978-1-78913-970-9 |
| spellingShingle | Lonza, Andrea Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges 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 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 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 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 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 Python Programmiersprache (DE-588)4434275-5 gnd Operante Konditionierung (DE-588)4172613-3 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4172613-3 |
| title | Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges |
| title_auth | Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges |
| title_exact_search | Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges |
| title_exact_search_txtP | Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges |
| title_full | Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges Andrea Lonza |
| title_fullStr | Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges Andrea Lonza |
| title_full_unstemmed | Reinforcement learning algorithms with Python learn, understand, and develop smart algorithms for addressing AI challenges Andrea Lonza |
| title_short | Reinforcement learning algorithms with Python |
| title_sort | reinforcement learning algorithms with python learn understand and develop smart algorithms for addressing ai challenges |
| title_sub | learn, understand, and develop smart algorithms for addressing AI challenges |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Operante Konditionierung (DE-588)4172613-3 gnd |
| topic_facet | Python Programmiersprache Operante Konditionierung |
| work_keys_str_mv | AT lonzaandrea reinforcementlearningalgorithmswithpythonlearnunderstandanddevelopsmartalgorithmsforaddressingaichallenges |