Reinforcement learning

As explained in Grokking AI Applications

Reinforcement learning is a machine learning approach in which an agent learns through trial and error while interacting with an environment to achieve goals.

Reinforcement learning lets an agent interact with an environment, try different actions, and adjust through success or failure while pursuing a goal. section 1.5

The three machine learning approaches differ by how learning signals are obtained:

• Unsupervised learning finds patterns without labels. section 1.5
• Supervised learning learns from labeled examples. section 1.5
• Reinforcement learning learns by interacting with an environment and responding to outcomes. section 1.5

Learning to walk

A child tries different movements and adjusts based on success or failure, illustrating reinforcement learning through trial and error. section 1.5

Figure 1.5 The three types of machine learning: unsupervised, supervised, and reinforcement

Use the comparison figure to distinguish reinforcement learning from supervised and unsupervised learning. section 1.5

How Grokking AI Applications covers it

How deep it goes

Defines trial and error and its role in ChatGPT refinement, but does not explain algorithms, rewards, or implementation.

Examples

Uses a child learning to walk and ChatGPT’s conversational refinement as examples.

What you can do after

Understand reinforcement learning’s role among ChatGPT’s three training stages.

How it connects

Ties reinforcement learning to machine learning, supervised learning, unsupervised learning, and ChatGPT training.

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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 Grokking AI Applications. The section links point to the parts of the book it draws on.