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Reinforcement Learning: Computer Science, Machine Learning, Game Theory, Bounded Rationality, Markov Decision Process, Supervised Learning, Multi-Armed Bandit, Robot Control, Monte Carlo Method -

English
2026-03-18
€149.47 €213.53

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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 t ... Full description

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Description

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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.

More Information

Publisher OmniScriptum
Release year 2026
Cover type Softcover
EAN 9786130348175
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€149.47 €213.53