Distributional Reinforcement Learning (Adaptive Computation and Machine Learning) - Image 1

£47.45

It has never been this cheap. We have no record of a lower price.

£47 today · cheaper than every other day in the last 3 months

MIT Press Distributional Reinforcement Learning (Adaptive Computation and Machine Learning)

Name: Distributional Reinforcement Learning (Adaptive Computation and Machine Learning)
Brand: MIT Press
Price: 47.45 GBP
Availability: InStock

White

Higher Education

Price Distribution

Price distribution over 36 days • 3 price levels

Days at Price

Current Price

Price Analysis

Most common price: £47 (21 days, 58.3%)

Price range: £47 - £51

Price levels: 3 different prices over 36 days

Description

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices--specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment. The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions.

Key Features

New Store Stock

Product Specifications

Brand: MIT Press
Colour: White
Format: hardcover
ASIN: 0262048019
Category: Books > Subjects > Science, Nature & Maths > Mathematics > Education > Higher Education
Domain: Amazon UK
Release Date: 30 May 2023
Listed Since: 20 September 2022

Barcode

No barcode data available

It has never been this cheap. We have no record of a lower price.

MIT Press Distributional Reinforcement Learning (Adaptive Computation and Machine Learning)

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Statistical Reinforcement Learning: Modern Machine Learning Approaches (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Reinforcement Learning: Mathematical Foundations and Algorithmic Principles

Reinforcement Learning Foundations

Reinforcement Learning: Industrial Applications of Intelligent Agents

Deep Reinforcement Learning in Action

Transfer in Reinforcement Learning Domains: 216 (Studies in Computational Intelligence, 216)

Grokking Deep Reinforcement Learning

Qualitative Spatial Abstraction in Reinforcement Learning (Cognitive Technologies)

Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making: 13 (Intelligent Systems, Control and Automation: Science and Engineering, 13)

Reinforcement Learning: 173 (The Springer International Series in Engineering and Computer Science, 173)

Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making: 13 (Intelligent Systems, Control and Automation: Science and Engineering, 13)

Decision Making Under Uncertainty – Theory and Application (MIT Lincoln Laboratory Series)

Deep Reinforcement Learning: Frontiers of Artificial Intelligence

Deep Reinforcement Learning: Frontiers of Artificial Intelligence

Bandit Algorithms

Real-World Machine Learning

Motivated Reinforcement Learning: Curious Characters for Multiuser Games

Stochastic Learning and Optimization: A Sensitivity-Based Approach

Mathematical Theories of Machine Learning - Theory and Applications

Density Ratio Estimation in Machine Learning

A First Course in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Learning with Uncertainty

Computational Learning and Probabilistic Reasoning

Markov Networks in Evolutionary Computation: 14 (Adaptation, Learning, and Optimization, 14)

It has never been this cheap. We have no record of a lower price.

MIT Press Distributional Reinforcement Learning (Adaptive Computation and Machine Learning)

Price History & Forecast

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Statistical Reinforcement Learning: Modern Machine Learning Approaches (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Reinforcement Learning: Mathematical Foundations and Algorithmic Principles

Reinforcement Learning Foundations

Reinforcement Learning: Industrial Applications of Intelligent Agents

Deep Reinforcement Learning in Action

Transfer in Reinforcement Learning Domains: 216 (Studies in Computational Intelligence, 216)

Grokking Deep Reinforcement Learning

Qualitative Spatial Abstraction in Reinforcement Learning (Cognitive Technologies)

Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making: 13 (Intelligent Systems, Control and Automation: Science and Engineering, 13)

Reinforcement Learning: 173 (The Springer International Series in Engineering and Computer Science, 173)

Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making: 13 (Intelligent Systems, Control and Automation: Science and Engineering, 13)

Decision Making Under Uncertainty – Theory and Application (MIT Lincoln Laboratory Series)

Deep Reinforcement Learning: Frontiers of Artificial Intelligence

Deep Reinforcement Learning: Frontiers of Artificial Intelligence

Bandit Algorithms

Real-World Machine Learning

Motivated Reinforcement Learning: Curious Characters for Multiuser Games

Stochastic Learning and Optimization: A Sensitivity-Based Approach

Mathematical Theories of Machine Learning - Theory and Applications

Density Ratio Estimation in Machine Learning

A First Course in Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Learning with Uncertainty

Computational Learning and Probabilistic Reasoning

Markov Networks in Evolutionary Computation: 14 (Adaptation, Learning, and Optimization, 14)