Reinforcement learning

As explained in Data Without Labels

A machine-learning paradigm in which an agent improves its behavior through sequential interactions, trial and error, and feedback from rewards or penalties.

Reinforcement learning is a machine-learning approach in which a system improves behavior through sequential interactions, trial and error, and reward signals. Positive outcomes encourage actions, while penalties prompt correction. section 1.5.4

The learning loop consists of:

• Taking an action in an ongoing sequence of interactions. section 1.5.4
• Receiving a positive or negative reward signal. section 1.5.4
• Correcting and improving later behavior iteratively. section 1.5.4

Chess and autonomous navigation

In a chess example, a computer first loses after several moves, then improves across repeated rounds until it wins. Self-driving cars provide another application: reinforcement learning helps determine when to turn, move, or stop. section 1.5.4 section 1.7

Figure 1.10 ML algorithms can be classified as supervised learning algorithms, unsupervised learning algorithms, semisupervised learning algorithms, and reinforcement learning algorithms.

Use the classification figure to compare reinforcement learning with supervised, unsupervised, and semisupervised learning. section 1.5

How Data Without Labels covers it

How deep it goes

Defines trial and error and reward signals, but stops short of algorithms, environments, or implementation details.

Examples

Uses computer chess, self-driving cars, and modern video games to illustrate sequential learning and action correction.

What it assumes

Assumes familiarity with machine-learning problem types and historical data as modeling input.

What you can do after

Evaluate reinforcement learning as a fit for dynamic decision-making and explain its reward-driven feedback loop.

How it connects

Connects reinforcement learning with supervised, unsupervised, semisupervised, and generative AI approaches.

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

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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 Data Without Labels. The section links point to the parts of the book it draws on.