Andrew G. Barto Books
Barto is an American computer scientist and professor emeritus at the University of Massachusetts Amherst, recognized for his contributions to machine learning and computational neuroscience.
Known for: Reinforcement Learning: An Introduction
Books by Andrew G. Barto
Reinforcement Learning: An Introduction
This foundational textbook provides a comprehensive introduction to reinforcement learning, a branch of machine learning concerned with how agents can learn to make decisions through interaction with their environment. It covers key concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, and policy gradient techniques. The book is widely used in academia and industry as a standard reference for understanding the theoretical and practical aspects of reinforcement learning.
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Formulating the Reinforcement Learning Problem with Markov Decision Processes
It always starts with the formulation. Every learning problem in reinforcement learning must be grounded in the interplay between the agent and the environment. This is captured neatly through the Markov Decision Process (MDP)—a mathematical model that binds states, actions, transitions, rewards, an...
From Reinforcement Learning: An Introduction
Dynamic Programming: Solving MDPs with Full Knowledge
Dynamic Programming (DP) is where reinforcement learning finds its computational heartbeat for problems with known models. When transitions and rewards are fully specified, we can exploit mathematical precision to compute optimal policies through iterative refinement. Policy evaluation calculates t...
From Reinforcement Learning: An Introduction
About Andrew G. Barto
Barto is an American computer scientist and professor emeritus at the University of Massachusetts Amherst, recognized for his contributions to machine learning and computational neuroscience.
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Barto is an American computer scientist and professor emeritus at the University of Massachusetts Amherst, recognized for his contributions to machine learning and computational neuroscience.
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