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Reinforcement Learning
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Geliefert zwischen Di., 11.11.2025 und Mi., 12.11.2025
Details
High Quality Content by WIKIPEDIA articles! Inspired by related psychological theory, in computer science, reinforcement learning is a sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. In economics and game theory, reinforcement learning is considered as a boundedly rational interpretation of how equilibrium may arise. The environment is typically formulated as a finite-state Markov decision process (MDP), and reinforcement learning algorithms for this context are highly related to dynamic programming techniques. State transition probabilities and reward probabilities in the MDP are typically stochastic but stationary over the course of the problem.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786130348175
- Editor Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken
- Sprache Englisch
- Größe H220mm x B150mm x T5mm
- Jahr 2010
- EAN 9786130348175
- Format Kartonierter Einband
- ISBN 978-613-0-34817-5
- Titel Reinforcement Learning
- Untertitel Computer Science, Machine Learning, Game Theory, Bounded Rationality, Markov Decision Process, Supervised Learning, Multi-Armed Bandit, Robot Control, Monte Carlo Method
- Gewicht 149g
- Herausgeber VDM Verlag Dr. Müller e.K.
- Anzahl Seiten 88
- Genre Informatik
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