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Reinforcement learning : an introduction / Richard S. Sutton and Andrew G. Barto

By: Contributor(s): Material type: TextTextSeries: Adaptive computation and machine learningPublisher: Cambridge, Massachusetts : MIT Press, [2018]Edition: Second editionDescription: 1 online resource : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
Subject(s): Genre/Form: Online resources:
Contents:
Reinforcement learning
Part I. Tabular solution methods
Multi-armed bandits
Finite Markov decision processes
Dynamic programming
Monte Carlo methods
Temporal-difference learning
n-step bootstrapping
Planning and learning with tabular methods
Part II. Approximate solution methods
On-policy prediction with approximation
On-policy control with approximation
Off-policy methods with approximation
Eligibility traces
Policy gradient methods
Part III. Looking deeper
Psychology
Neuroscience
Applications and case studies
Frontiers
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eBook (Free & Open Access) eBook (Free & Open Access) Accessible online Circulation Available EB-00238

Includes bibliographical references and index.

Reinforcement learning

Part I. Tabular solution methods

Multi-armed bandits

Finite Markov decision processes

Dynamic programming

Monte Carlo methods

Temporal-difference learning

n-step bootstrapping

Planning and learning with tabular methods

Part II. Approximate solution methods

On-policy prediction with approximation

On-policy control with approximation

Off-policy methods with approximation

Eligibility traces

Policy gradient methods

Part III. Looking deeper

Psychology

Neuroscience

Applications and case studies

Frontiers

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