Deep reinforcement learning with Python :: master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow /
Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures - including deep reinforcement learning - from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinkin...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing Ltd.,
2020.
|
| Ausgabe: | Second edition. |
| Schriftenreihe: | Expert insight.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures - including deep reinforcement learning - from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinking in reinforcement learning. |
| Beschreibung: | Previous edition published in 2018. The blackjack environment in the Gym library. |
| Beschreibung: | 1 online resource (xxi, 730 pages) : illustrations |
| ISBN: | 9781839215599 1839215593 |
Internformat
MARC
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| 245 | 1 | 0 | |a Deep reinforcement learning with Python : |b master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / |c Sudharsan Ravichandiran. |
| 250 | |a Second edition. | ||
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| 500 | |a Previous edition published in 2018. | ||
| 505 | 0 | |a Cover -- Copyright -- Packt Page -- Contributors -- Table of Contents -- Preface -- Chapter 1: Fundamentals of Reinforcement Learning -- Key elements of RL -- Agent -- Environment -- State and action -- Reward -- The basic idea of RL -- The RL algorithm -- RL agent in the grid world -- How RL differs from other ML paradigms -- Markov Decision Processes -- The Markov property and Markov chain -- The Markov Reward Process -- The Markov Decision Process -- Fundamental concepts of RL -- Math essentials -- Expectation -- Action space -- Policy -- Deterministic policy -- Stochastic policy | |
| 505 | 8 | |a Episode -- Episodic and continuous tasks -- Horizon -- Return and discount factor -- Small discount factor -- Large discount factor -- What happens when we set the discount factor to 0? -- What happens when we set the discount factor to 1? -- The value function -- Q function -- Model-based and model-free learning -- Different types of environments -- Deterministic and stochastic environments -- Discrete and continuous environments -- Episodic and non-episodic environments -- Single and multi-agent environments -- Applications of RL -- RL glossary -- Summary -- Questions -- Further reading | |
| 505 | 8 | |a Chapter 2: A Guide to the Gym Toolkit -- Setting up our machine -- Installing Anaconda -- Installing the Gym toolkit -- Common error fixes -- Creating our first Gym environment -- Exploring the environment -- States -- Actions -- Transition probability and reward function -- Generating an episode in the Gym environment -- Action selection -- Generating an episode -- More Gym environments -- Classic control environments -- State space -- Action space -- Cart-Pole balancing with random policy -- Atari game environments -- General environment -- Deterministic environment -- No frame skipping | |
| 505 | 8 | |a State and action space -- An agent playing the Tennis game -- Recording the game -- Other environments -- Box2D -- MuJoCo -- Robotics -- Toy text -- Algorithms -- Environment synopsis -- Summary -- Questions -- Further reading -- Chapter 3: The Bellman Equation and Dynamic Programming -- The Bellman equation -- The Bellman equation of the value function -- The Bellman equation of the Q function -- The Bellman optimality equation -- The relationship between the value and Q functions -- Dynamic programming -- Value iteration -- The value iteration algorithm | |
| 505 | 8 | |a Solving the Frozen Lake problem with value iteration -- Policy iteration -- Algorithm -- policy iteration -- Solving the Frozen Lake problem with policy iteration -- Is DP applicable to all environments? -- Summary -- Questions -- Chapter 4: Monte Carlo Methods -- Understanding the Monte Carlo method -- Prediction and control tasks -- Prediction task -- Control task -- Monte Carlo prediction -- MC prediction algorithm -- Types of MC prediction -- First-visit Monte Carlo -- Every-visit Monte Carlo -- Implementing the Monte Carlo prediction method -- Understanding the blackjack game | |
| 500 | |a The blackjack environment in the Gym library. | ||
| 520 | |a Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures - including deep reinforcement learning - from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinking in reinforcement learning. | ||
| 650 | 0 | |a Reinforcement learning. |0 http://id.loc.gov/authorities/subjects/sh92000704 | |
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 6 | |a Apprentissage par renforcement (Intelligence artificielle) | |
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| 776 | 0 | 8 | |i Print version: |a Ravichandiran, Sudharsan |t Deep Reinforcement Learning with Python : Master Classic RL, Deep RL, Distributional RL, Inverse RL, and More with OpenAI Gym and TensorFlow, 2nd Edition |d Birmingham : Packt Publishing, Limited,c2020 |z 9781839210686 |
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| contents | Cover -- Copyright -- Packt Page -- Contributors -- Table of Contents -- Preface -- Chapter 1: Fundamentals of Reinforcement Learning -- Key elements of RL -- Agent -- Environment -- State and action -- Reward -- The basic idea of RL -- The RL algorithm -- RL agent in the grid world -- How RL differs from other ML paradigms -- Markov Decision Processes -- The Markov property and Markov chain -- The Markov Reward Process -- The Markov Decision Process -- Fundamental concepts of RL -- Math essentials -- Expectation -- Action space -- Policy -- Deterministic policy -- Stochastic policy Episode -- Episodic and continuous tasks -- Horizon -- Return and discount factor -- Small discount factor -- Large discount factor -- What happens when we set the discount factor to 0? -- What happens when we set the discount factor to 1? -- The value function -- Q function -- Model-based and model-free learning -- Different types of environments -- Deterministic and stochastic environments -- Discrete and continuous environments -- Episodic and non-episodic environments -- Single and multi-agent environments -- Applications of RL -- RL glossary -- Summary -- Questions -- Further reading Chapter 2: A Guide to the Gym Toolkit -- Setting up our machine -- Installing Anaconda -- Installing the Gym toolkit -- Common error fixes -- Creating our first Gym environment -- Exploring the environment -- States -- Actions -- Transition probability and reward function -- Generating an episode in the Gym environment -- Action selection -- Generating an episode -- More Gym environments -- Classic control environments -- State space -- Action space -- Cart-Pole balancing with random policy -- Atari game environments -- General environment -- Deterministic environment -- No frame skipping State and action space -- An agent playing the Tennis game -- Recording the game -- Other environments -- Box2D -- MuJoCo -- Robotics -- Toy text -- Algorithms -- Environment synopsis -- Summary -- Questions -- Further reading -- Chapter 3: The Bellman Equation and Dynamic Programming -- The Bellman equation -- The Bellman equation of the value function -- The Bellman equation of the Q function -- The Bellman optimality equation -- The relationship between the value and Q functions -- Dynamic programming -- Value iteration -- The value iteration algorithm Solving the Frozen Lake problem with value iteration -- Policy iteration -- Algorithm -- policy iteration -- Solving the Frozen Lake problem with policy iteration -- Is DP applicable to all environments? -- Summary -- Questions -- Chapter 4: Monte Carlo Methods -- Understanding the Monte Carlo method -- Prediction and control tasks -- Prediction task -- Control task -- Monte Carlo prediction -- MC prediction algorithm -- Types of MC prediction -- First-visit Monte Carlo -- Every-visit Monte Carlo -- Implementing the Monte Carlo prediction method -- Understanding the blackjack game |
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| discipline | Informatik |
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| indexdate | 2024-11-27T13:30:06Z |
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| isbn | 9781839215599 1839215593 |
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| spelling | Ravichandiran, Sudharsan, author. Deep reinforcement learning with Python : master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / Sudharsan Ravichandiran. Second edition. Birmingham, UK : Packt Publishing Ltd., 2020. 1 online resource (xxi, 730 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Expert insight Previous edition published in 2018. Cover -- Copyright -- Packt Page -- Contributors -- Table of Contents -- Preface -- Chapter 1: Fundamentals of Reinforcement Learning -- Key elements of RL -- Agent -- Environment -- State and action -- Reward -- The basic idea of RL -- The RL algorithm -- RL agent in the grid world -- How RL differs from other ML paradigms -- Markov Decision Processes -- The Markov property and Markov chain -- The Markov Reward Process -- The Markov Decision Process -- Fundamental concepts of RL -- Math essentials -- Expectation -- Action space -- Policy -- Deterministic policy -- Stochastic policy Episode -- Episodic and continuous tasks -- Horizon -- Return and discount factor -- Small discount factor -- Large discount factor -- What happens when we set the discount factor to 0? -- What happens when we set the discount factor to 1? -- The value function -- Q function -- Model-based and model-free learning -- Different types of environments -- Deterministic and stochastic environments -- Discrete and continuous environments -- Episodic and non-episodic environments -- Single and multi-agent environments -- Applications of RL -- RL glossary -- Summary -- Questions -- Further reading Chapter 2: A Guide to the Gym Toolkit -- Setting up our machine -- Installing Anaconda -- Installing the Gym toolkit -- Common error fixes -- Creating our first Gym environment -- Exploring the environment -- States -- Actions -- Transition probability and reward function -- Generating an episode in the Gym environment -- Action selection -- Generating an episode -- More Gym environments -- Classic control environments -- State space -- Action space -- Cart-Pole balancing with random policy -- Atari game environments -- General environment -- Deterministic environment -- No frame skipping State and action space -- An agent playing the Tennis game -- Recording the game -- Other environments -- Box2D -- MuJoCo -- Robotics -- Toy text -- Algorithms -- Environment synopsis -- Summary -- Questions -- Further reading -- Chapter 3: The Bellman Equation and Dynamic Programming -- The Bellman equation -- The Bellman equation of the value function -- The Bellman equation of the Q function -- The Bellman optimality equation -- The relationship between the value and Q functions -- Dynamic programming -- Value iteration -- The value iteration algorithm Solving the Frozen Lake problem with value iteration -- Policy iteration -- Algorithm -- policy iteration -- Solving the Frozen Lake problem with policy iteration -- Is DP applicable to all environments? -- Summary -- Questions -- Chapter 4: Monte Carlo Methods -- Understanding the Monte Carlo method -- Prediction and control tasks -- Prediction task -- Control task -- Monte Carlo prediction -- MC prediction algorithm -- Types of MC prediction -- First-visit Monte Carlo -- Every-visit Monte Carlo -- Implementing the Monte Carlo prediction method -- Understanding the blackjack game The blackjack environment in the Gym library. Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures - including deep reinforcement learning - from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinking in reinforcement learning. Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage par renforcement (Intelligence artificielle) Python (Langage de programmation) Python (Computer program language) fast Reinforcement learning fast has work: Deep reinforcement learning with Python (Text) https://id.oclc.org/worldcat/entity/E39PCG9CRGRdtwWk3YpKfxQT9C https://id.oclc.org/worldcat/ontology/hasWork Print version: Ravichandiran, Sudharsan Deep Reinforcement Learning with Python : Master Classic RL, Deep RL, Distributional RL, Inverse RL, and More with OpenAI Gym and TensorFlow, 2nd Edition Birmingham : Packt Publishing, Limited,c2020 9781839210686 Expert insight. http://id.loc.gov/authorities/names/no2019019794 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2640444 Volltext |
| spellingShingle | Ravichandiran, Sudharsan Deep reinforcement learning with Python : master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / Expert insight. Cover -- Copyright -- Packt Page -- Contributors -- Table of Contents -- Preface -- Chapter 1: Fundamentals of Reinforcement Learning -- Key elements of RL -- Agent -- Environment -- State and action -- Reward -- The basic idea of RL -- The RL algorithm -- RL agent in the grid world -- How RL differs from other ML paradigms -- Markov Decision Processes -- The Markov property and Markov chain -- The Markov Reward Process -- The Markov Decision Process -- Fundamental concepts of RL -- Math essentials -- Expectation -- Action space -- Policy -- Deterministic policy -- Stochastic policy Episode -- Episodic and continuous tasks -- Horizon -- Return and discount factor -- Small discount factor -- Large discount factor -- What happens when we set the discount factor to 0? -- What happens when we set the discount factor to 1? -- The value function -- Q function -- Model-based and model-free learning -- Different types of environments -- Deterministic and stochastic environments -- Discrete and continuous environments -- Episodic and non-episodic environments -- Single and multi-agent environments -- Applications of RL -- RL glossary -- Summary -- Questions -- Further reading Chapter 2: A Guide to the Gym Toolkit -- Setting up our machine -- Installing Anaconda -- Installing the Gym toolkit -- Common error fixes -- Creating our first Gym environment -- Exploring the environment -- States -- Actions -- Transition probability and reward function -- Generating an episode in the Gym environment -- Action selection -- Generating an episode -- More Gym environments -- Classic control environments -- State space -- Action space -- Cart-Pole balancing with random policy -- Atari game environments -- General environment -- Deterministic environment -- No frame skipping State and action space -- An agent playing the Tennis game -- Recording the game -- Other environments -- Box2D -- MuJoCo -- Robotics -- Toy text -- Algorithms -- Environment synopsis -- Summary -- Questions -- Further reading -- Chapter 3: The Bellman Equation and Dynamic Programming -- The Bellman equation -- The Bellman equation of the value function -- The Bellman equation of the Q function -- The Bellman optimality equation -- The relationship between the value and Q functions -- Dynamic programming -- Value iteration -- The value iteration algorithm Solving the Frozen Lake problem with value iteration -- Policy iteration -- Algorithm -- policy iteration -- Solving the Frozen Lake problem with policy iteration -- Is DP applicable to all environments? -- Summary -- Questions -- Chapter 4: Monte Carlo Methods -- Understanding the Monte Carlo method -- Prediction and control tasks -- Prediction task -- Control task -- Monte Carlo prediction -- MC prediction algorithm -- Types of MC prediction -- First-visit Monte Carlo -- Every-visit Monte Carlo -- Implementing the Monte Carlo prediction method -- Understanding the blackjack game Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage par renforcement (Intelligence artificielle) Python (Langage de programmation) Python (Computer program language) fast Reinforcement learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh92000704 http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Deep reinforcement learning with Python : master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / |
| title_auth | Deep reinforcement learning with Python : master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / |
| title_exact_search | Deep reinforcement learning with Python : master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / |
| title_full | Deep reinforcement learning with Python : master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / Sudharsan Ravichandiran. |
| title_fullStr | Deep reinforcement learning with Python : master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / Sudharsan Ravichandiran. |
| title_full_unstemmed | Deep reinforcement learning with Python : master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / Sudharsan Ravichandiran. |
| title_short | Deep reinforcement learning with Python : |
| title_sort | deep reinforcement learning with python master classic rl deep rl distributional rl inverse rl and more with openai gym and tensorflow |
| title_sub | master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow / |
| topic | Reinforcement learning. http://id.loc.gov/authorities/subjects/sh92000704 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage par renforcement (Intelligence artificielle) Python (Langage de programmation) Python (Computer program language) fast Reinforcement learning fast |
| topic_facet | Reinforcement learning. Python (Computer program language) Apprentissage par renforcement (Intelligence artificielle) Python (Langage de programmation) Reinforcement learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2640444 |
| work_keys_str_mv | AT ravichandiransudharsan deepreinforcementlearningwithpythonmasterclassicrldeeprldistributionalrlinverserlandmorewithopenaigymandtensorflow |