Muutke küpsiste eelistusi

E-raamat: Reinforcement Learning From Scratch: Understanding Current Approaches - with Examples in Java and Greenfoot

  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Oct-2022
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783031090301
  • Formaat - PDF+DRM
  • Hind: 67,91 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Oct-2022
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783031090301

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? 





With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Kölling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. 

The result is an accessible introduction into machine learning that  concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.  
1 Reinforcement Learning as a Subfield of Machine Learning
1(14)
1.1 Machine Learning as Automated Processing of Feedback from the Environment
2(1)
1.2 Machine Learning
3(5)
1.3 Reinforcement Learning with Java
8(7)
Bibliography
13(2)
2 Basic Concepts of Reinforcement Learning
15(8)
2.1 Agents
16(2)
2.2 The Policy of the Agent
18(2)
2.3 Evaluation of States and Actions (Q-Function, Bellman Equation)
20(3)
Bibliography
22(1)
3 Optimal Decision-Making in a Known Environment
23(24)
3.1 Value Iteration
25(11)
3.1.1 Target-Oriented Condition Assessment ("Backward Induction")
25(9)
3.1.2 Policy-Based State Valuation (Reward Prediction)
34(2)
3.2 Iterative Policy Search
36(7)
3.2.1 Direct Policy Improvement
37(1)
3.2.2 Mutual Improvement of Policy and Value Function
38(5)
3.3 Optimal Policy in a Board Game Scenario
43(3)
3.4 Summary
46(1)
Bibliography
46(1)
4 Decision-Making and Learning in an Unknown Environment
47(76)
4.1 Exploration vs. Exploitation
49(2)
4.2 Retroactive Processing of Experience ("Model-Free Reinforcement Learning")
51(45)
4.2.1 Goal-Oriented Learning ("Value-Based")
51(15)
4.2.2 Policy Search
66(18)
4.2.3 Combined Methods (Actor-Critic)
84(12)
4.3 Exploration with Predictive Simulations ("Model-Based Reinforcement Learning")
96(24)
4.3.1 Dyna-Q
97(4)
4.3.2 Monte Carlo Rollout
101(6)
4.3.3 Artificial Curiosity
107(4)
4.3.4 Monte Carlo Tree Search (MCTS)
111(7)
4.3.5 Remarks on the Concept of Intelligence
118(2)
4.4 Systematics of the Learning Methods
120(3)
Bibliography
121(2)
5 Artificial Neural Networks as Estimators for State Values and the Action Selection
123(52)
5.1 Artificial Neural Networks
125(27)
5.1.1 Pattern Recognition with the Perceptron
128(3)
5.1.2 The Adaptability of Artificial Neural Networks
131(15)
5.1.3 Backpropagation Learning
146(3)
5.1.4 Regression with Multilayer Perceptrons
149(3)
5.2 State Evaluation with Generalizing Approximations
152(11)
5.3 Neural Estimators for Action Selection
163(12)
5.3.1 Policy Gradient with Neural Networks
163(2)
5.3.2 Proximal Policy Optimization
165(4)
5.3.3 Evolutionary Strategy with a Neural Policy
169(4)
Bibliography
173(2)
6 Guiding Ideas in Artificial Intelligence over Time
175(9)
6.1 Changing Guiding Ideas
176(5)
6.2 On the Relationship Between Humans and Artificial Intelligence
181(3)
Bibliography 184
After studying computer science and philosophy with a focus on artificial intelligence and machine learning at the Humboldt University Berlin and for a few years as a project engineer, Uwe Lorenz currently works as a high school teacher for computer science and mathematics and at the Free University of Berlin in the Computing Education Research Group, - since his first contact with computers at the end of the 1980s he couldn't let go of the topic of artificial intelligence.