Reinforcement learning

As explained in Inside AI

Reinforcement learning is a machine-learning technique in which an agent learns behavior by interacting with an environment and receiving feedback for its actions.

How Inside AI covers it

How deep it goes

Defines trial-and-error learning and reinforcement signals, then illustrates self-play without detailing implementation.

Examples

Uses Arthur Samuel’s 1956 self-playing checkers program as its concrete example.

What you can do after

Evaluate whether an application has sufficiently clear rewards to justify reinforcement learning and identify the need for oversight.

How it connects

Connects machine learning with trial and error, rewards and punishments, cybernetics, and self-play.

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Also explained in

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  Reinforcement Learning from Human FeedbackReinforcement learning is a machine learning paradigm in which an agent learns behavior by optimizing rewards obtained through interaction with an environment.
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  Optimization Algorithms: AI techniques for design, planning, and control problemsReinforcement learning is a machine-learning paradigm in which an agent learns action policies from interactions and evaluative feedback.
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  Evolutionary Deep Learning: Genetic algorithms and neural networksReinforcement learning is a machine-learning paradigm in which an agent learns behavior by interacting with an environment and receiving rewards or other evaluative feedback.
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  Sutskever's ListMachine learning in which an agent learns behavior through rewards or penalties from interaction with an environment.
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  Deep Learning with R, Third EditionReinforcement learning is a machine-learning technique in which an agent learns behavior through interaction with an environment and feedback such as rewards.
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  Build a Robo-Advisor with Python (From Scratch)Reinforcement learning is a machine-learning approach in which an agent learns a decision policy through interactions with an environment and feedback from rewards or penalties.
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  Grokking AI Algorithms, Second EditionReinforcement learning is a machine-learning paradigm in which an agent learns behavior through rewards for useful actions and penalties for harmful or unsuccessful actions in an environment.
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  The AI Pocket BookA learning approach in which an agent improves by trying actions and using feedback from the results.
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  Build a Reasoning Model (From Scratch)A training approach where a model learns from rewards or feedback to improve its behavior.
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  Introduction to Generative AI, Second EditionReinforcement learning is machine learning in which an agent learns behavior from rewards or penalties produced by interactions with an environment.
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  Grokking AI ApplicationsReinforcement learning is a machine learning approach in which an agent learns through trial and error while interacting with an environment to achieve goals.
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  Deep Learning with Python, Third EditionA learning setup where an agent improves by interacting with an environment and receiving feedback, used here as a way to keep generating fresh training problems for LLMs.
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  Grokking Machine LearningA machine-learning approach in which an agent learns to choose actions by interacting with an environment and receiving rewards or punishments.
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  Acing the CCNA Exam: Volume 2 Advanced Networking and SecurityReinforcement learning is a machine-learning approach in which an agent learns behavior through interactions with an environment and feedback about its actions.
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  Causal AIA machine-learning paradigm in which an agent learns actions or policies by interacting with an environment and receiving rewards or penalties.
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  Data Without LabelsA machine-learning paradigm in which an agent improves its behavior through sequential interactions, trial and error, and feedback from rewards or penalties.
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  Data-Driven CybersecurityReinforcement learning is a machine-learning approach in which an agent learns decisions by interacting with an environment and optimizing cumulative feedback.

Compare how 18 books explain reinforcement learning →

Related concepts

• reward signaluses · A reward signal is an evaluative feedback value representing the desirability or payoff of an action, outcome, or state.
• machine learningkind of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
• AI agentuses · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
• environmentuses · An environment is the external context, including its entities and conditions, in which a system, process, or agent exists or operates.
• reinforcement learning with human feedbackused by · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.
• fine-tuningcontrasts with · Fine-tuning is adapting a pretrained machine-learning model by training it further on targeted data or objectives for a task, domain, or behavior.

This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.