Page type: Article / Wiki · Category: Computer science / Artificial intelligence

Reinforcement Learning (Introduction)

Reinforcement learning studies agents that improve a policy by interacting with an environment and receiving a reward signal.

Overview

Reinforcement learning studies agents that improve a policy by interacting with an environment and receiving a reward signal.

Agents optimize the reward you wrote, not the wish you had. Misspecified rewards produce odd behaviour. That is a core wiki point, not a joke.

Definition

State, action, reward, policy are the usual pieces. The environment may be a simulator. Real-world action has safety constraints this page does not waive.

Unlike supervised learning, the “right answer” may never be shown; only a scalar or sparse reward arrives, often later.

This is an introduction. It is not a game-cheat guide.

Why the distinction matters

If you reward speed, you may get reckless policies. If you reward a proxy, you will get the proxy.

Simulators omit the parts of the world that hurt. Transfer is a research problem, not a checkbox.

Core pieces

An environment API.

A reward function.
A policy (and often a value estimate).
An exploration strategy.

A safety envelope if actions are real.

If a tutorial skips these pieces and jumps to a demo, you are watching a product, not reading a definition.

Worked intuition

A robot rewarded for distance traveled may spin in circles if that accumulates the reward. You get what you measure.

Board-game RL with perfect rules is a different object from a robot near people. Do not copy headlines across that gap.

Common confusions

Calling any adaptive system RL.
Hiding the reward function in a paper about “alignment.”

Training in a toy and deploying on a street without a new evaluation.
Ignoring sample inefficiency as if compute were free.

Limits

Reward design is hard. Safety constraints belong in the environment definition if actions can harm.

Exploration can take catastrophic actions unless constrained.

Practical checks

Write the reward in one paragraph a critic can attack.
Constrain the action space before you “see what happens.”

Report environment versions.
Do not test first in an irreversible setting.

What a careful page refuses

It refuses fake precision, fake timelines, and vendor adjectives that are not part of the definition.

Exploration can take catastrophic actions unless constrained.

See also: supervised learning (different setting), limitations of current AI.

Glossary

Policy: mapping from states to actions (or to distributions over actions).
Reward: the scalar (or structured) signal the agent increases.
Exploration: trying actions that may look suboptimal now.

How to use this wiki page

Read the definition, then the confusions, then the checks. The FAQ is last on purpose: it should not replace the definition.

If you cite this page, cite the limitation that matches your use, not only the first sentence.

FAQ

Is AlphaGo the same as a warehouse robot?

Same family of ideas, different constraints. Do not import the hype.

Can I RLHF my way out of a bad task definition?

Human feedback is another reward. It can be misspecified too.

Is this a games-cheat guide?

No. This page is about a learning paradigm in computer science.

Why this page exists in the collection

Reinforcement Learning (Introduction) sits in a Article / Wiki slot with category Computer science / Artificial intelligence. That pairing is not decoration: readers should be able to tell a research note from a listing, and a home page from a wiki overview, before they quote a sentence out of context.

The one-line job of the page is this: Wiki introduction to reinforcement learning: agents, rewards, and why the reward is the hard part.

If you only remember one constraint, remember the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

The page is written for computer science readers who will either teach from it, cite it, or use it as a map. It is not written as a press release and it does not invent measurements that were not collected.

Scope and non-scope, stated slowly

In scope: the practice and documents around Computer science, Artificial intelligence, reinforcement learning, agents. Out of scope: ranking offices, promising outcomes, or turning a classroom into a market.

A useful test is whether a sentence still holds if you remove adjectives. “An environment API.” is the kind of object this page is willing to talk about because it can be pointed at.

Another object on the table is “A reward function.”. If your question is actually about something else—private casework, live filings, clinical advice, or product pricing—stop and go to a qualified channel.

Non-scope also includes gossip about named minors, unnamed “secret” datasets, and any request to hide a limitation because it makes the story less tidy.

Walking through the checklist in full sentences

Item 1. An environment API. Treat this as something you could put on a table in a meeting about Reinforcement Learning (Introduction). If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 2. A reward function. Treat this as something you could put on a table in a meeting about Reinforcement Learning (Introduction). If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 3. A policy (and often a value estimate). Treat this as something you could put on a table in a meeting about Reinforcement Learning (Introduction). If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 4. An exploration strategy. Treat this as something you could put on a table in a meeting about Reinforcement Learning (Introduction). If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 5. A safety envelope if actions are real. Treat this as something you could put on a table in a meeting about Reinforcement Learning (Introduction). If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 6. Calling any adaptive system RL. Treat this as something you could put on a table in a meeting about Reinforcement Learning (Introduction). If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 7. Hiding the reward function in a paper about “alignment.” Treat this as something you could put on a table in a meeting about Reinforcement Learning (Introduction). If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 8. Training in a toy and deploying on a street without a new evaluation. Treat this as something you could put on a table in a meeting about Reinforcement Learning (Introduction). If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

A longer narrative of the problem

People usually meet Reinforcement Learning (Introduction) as a short slogan. The slogan travels faster than the log. Then a team is surprised when a term ends and the only remaining trace is a folder of unused files.

The longer story is operational. Someone has to name the text, the hour, the owner, and the thing students or readers will produce. Without that, Computer science, Artificial intelligence, reinforcement learning, agents becomes wallpaper.

Consider a week in which An environment API. is supposed to happen, but A reward function. is competing for the same hour. The honest publication names the collision instead of adding a new poster.

Consider also the quiet failure: the work is done, but nobody can find it next month because the filename is “final-final-v3”. Documentation is part of the method, not an afterthought for Reinforcement Learning (Introduction).

None of this requires a new brand of software. It requires a calendar, a named artifact, and a sentence about what will not be claimed. That is the tone of this page.

Worked scenario A: a careful trial

A small team decides to trial one idea from Reinforcement Learning (Introduction) for four weeks, not a year. They write the question in one sentence copied from the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

Week 1 is setup: they identify the artifact that will count as “done.” It should be as concrete as An environment API.. They also write the exclusion: they will not claim effects they did not measure.

Week 2 is the first real run. They expect friction around A reward function.. They log what was skipped and why, in language a substitute colleague could understand.

Week 3 is a repair week. They drop one extra ambition so A policy (and often a value estimate). can actually finish. Repair is not failure; it is the method.

Week 4 is a write-up of two pages: what happened, what they will keep, what they will not repeat. They cite this page as a map, not as proof.

Worked scenario B: the over-scoped version that fails

A different team announces Reinforcement Learning (Introduction) as a whole-institution priority in the same week they have reports, a public event, and a system migration. Nothing is named as the single artifact.

They create a dashboard. The dashboard cannot answer whether An environment API. occurred. It can only show that a file was uploaded.

By week six the original lead—Page type: Article / Wiki · Category: Computer science / Artificial intelligence—is no longer mentioned in meetings. People mention “the initiative.” Initiatives do not leave notebooks.

The recovery is embarrassing and simple: shrink back to one unit, one owner, one collected task, and the limits already written on this page.

A twelve-week implementation sketch

Week 1: Name the question Reinforcement Learning (Introduction) is actually asking.
Week 2: Inventory current documents related to Computer science, Artificial intelligence, reinforcement learning, agents.
Week 3: Pick one artifact as concrete as: An environment API..

Week 4: Write the non-claims in language copied from this page’s limits.
Week 5: Run a tiny version that still includes A reward function..
Week 6: Log skips; do not hide them in a highlight reel.

Week 7: Repair the calendar so A policy (and often a value estimate). can finish.
Week 8: Share a two-page note with a colleague who was not in the room.

Week 9: Decide whether to stop, continue, or redesign.
Week 10: If continuing, freeze the definition of “done” for the next month.
Week 11: Check that citations still point at dated sources, not at rumours.

Week 12: Retire leftover files that contradict the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

This calendar is a sketch for Reinforcement Learning (Introduction), not a contract. If a public deadline in computer science collides with a week, move the week—do not pretend both happened.

If you skip logging, you are back to slogans. The sketch exists to make skipping visible.

Documentation pack

A one-sentence question taken from Reinforcement Learning (Introduction).
The dated lead as published: Page type: Article / Wiki · Category: Computer science / Artificial intelligence
A list of in-scope objects, starting with An environment API..

A list of out-of-scope requests (advice, rankings, invented rates).
Names of owners for A reward function. and a substitute if they are away.
A filename convention that includes a date.

A citation line that includes limits.
Links to sibling pages in Computer science.
A retirement note for superseded files.

A short glossary so newcomers do not invent synonyms.

If the pack cannot fit in a folder a new colleague can open in five minutes, it is too baroque for Reinforcement Learning (Introduction).

Pretty templates are optional. Dates and owners are not.

Error catalog

Publishing identifiable information that the method said to remove.
Asking the page to do casework, medical advice, or live filings.

Mixing page type Article / Wiki with a different genre in the same citation.
Letting an undated PDF outrank the dated page.
Treating An environment API. as optional theatre while keeping the slogan.

Hiding the collision between A reward function. and a hard calendar event.
Quoting Reinforcement Learning (Introduction) as if it measured an outcome it explicitly refused to measure.
Inventing a percentage because a meeting wanted a percentage.

Scaling across all of computer science before a four-week trial exists.
Citing an unofficial look-alike domain as the primary source.

Each error is recoverable if you name it early. It is expensive if it becomes the public story of the work.

The cheapest prevention for Reinforcement Learning (Introduction) is to reread the non-claims before you present.

Glossary for this page

Reinforcement Learning (Introduction) — the document you are reading, with page type Article / Wiki and category Computer science / Artificial intelligence.

Artifact — a thing you could hold up, such as: An environment API.
Lead — the opening claim: Page type: Article / Wiki · Category: Computer science / Artificial intelligence
Limit — a sentence that forbids a nicer claim than the method can carry.

Computer science — the home section of this page, not a licence to speak for every office in the world.
Date — the difference between a publication and a rumour.
Owner — the person who can change A reward function. without a mystery committee.

Sibling page — another title in the same section, listed below when available.

Reader checklist before you cite or adopt

Can you state the job of Reinforcement Learning (Introduction) without adjectives?
Can you point at An environment API. in a real folder or classroom?

Is every number (if any) sourced, or did you add none because none were collected?
Does the citation include the limit that belongs with Computer science, Artificial intelligence, reinforcement learning, agents?
Would a substitute colleague know what “done” looks like next week?

Have you avoided promising a ranking, a cure, or a guaranteed placement?
Is the page type still honestly Article / Wiki?
Is the category still honestly Computer science / Artificial intelligence?

If you fail two checks, do not cite yet. Fix the file or shrink the claim.

This checklist is part of Reinforcement Learning (Introduction), not a generic poster.

What “good enough” looks like without fake scores

Good enough for Reinforcement Learning (Introduction) is a dated artifact, a named owner, and a next step that survived contact with a calendar.

It is not a launch photograph. It is not a dashboard that cannot answer whether An environment API. happened.

It is certainly not a claim that Computer science, Artificial intelligence, reinforcement learning, agents has been “solved.” Solved is a word this collection tries not to use.

If you need a number, collect one that matches the question, then publish the instrument. Until then, write in sentences.

Teaching notes

If you teach Reinforcement Learning (Introduction), give students a primary object first: a form, a lab page, a syllabus line, a model card, a gazette. Then give them this page as a map of how to talk about that object.

A good thirty-minute seminar: (1) read the lead, (2) mark the non-claims, (3) try to apply An environment API. to a public document you did not write.

Do not ask students to harvest private data. Do not ask them to impersonate an office. Do not ask them to produce a rate you would not defend.

Assessment can be a two-page memo that cites this page and one official source, with the date of capture written on the first line. That is enough to see whether computer science literacy is happening.

For information officers and editors

If you maintain public pages in computer science, steal the habits, not the adjectives: date, owner, next step, non-claim.

Reinforcement Learning (Introduction) will age. Put a review month on it. If you cannot review it, do not let it remain the featured link.

When legal, medical, or emergency readers arrive, your first job is to send them to a qualified channel. Education pages that pretend to be those channels cause harm.

When you quote Reinforcement Learning (Introduction) in a newsletter, quote a limit next to the attractive sentence. Attractive sentences travel; limits do not, unless you chain them.

Notes on wiki genre

A wiki overview defines, distinguishes, and lists failure modes. It does not sell a library or a timeline to imaginary general intelligence.

Reinforcement Learning (Introduction) should be cited for the distinction it draws, not as proof that a product works.

If a tutorial skips evaluation and jumps to a demo, it is not this page.

Update the glossary if a word starts meaning three things in your course. Do not pretend the field is settled.

Related pages in this collection

Evaluation Metrics in Machine Learning — Wiki overview of evaluation metrics: accuracy is not always the right score, and the split matters.
Overfitting and Regularization — Wiki article on overfitting: fitting the training sample too closely, and regularization as a family of restraints.

Limitations of Current AI Systems — Wiki-style catalog of limitations: distribution shift, hallucinations in generation, and cost of evaluation.
Feature Representation — Wiki-style page on feature representation: how raw inputs become the vectors a model consumes.
Unsupervised Learning — Wiki article on unsupervised learning: finding structure in data without task labels.

These titles share the Computer science section with Reinforcement Learning (Introduction). They are not duplicates. Read the page type before you mix citations.

If a sibling contradicts this page, prefer the dated limits on each page rather than blending them into a mash-up claim.

Plain-language recap

Reinforcement Learning (Introduction) is a Article / Wiki page in Computer science / Artificial intelligence. Its job is: Wiki introduction to reinforcement learning: agents, rewards, and why the reward is the hard part.

Do the concrete thing (An environment API.). Write down what you will not claim. Date the file. Name an owner for A reward function..

Do not invent rates. Do not use this page as a clinic, a court, or a marketplace. Do not strip the limits off the attractive sentences.

If you do only that, the collection has done enough work for one reading.

Versioning and review

When you locally adapt Reinforcement Learning (Introduction), keep a version line: date, editor, what changed, what did not.

A change to the lead is a new document. A change to an example can be a minor note.

Review at least when the surrounding computer science calendar jumps (new term, new statute text, new dataset version).

If nobody is named to review it, the page is already on its way to becoming folklore.