--- title: A visual explanation of reinforcement learning | daily.dev description: This interactive explainer lets you watch a simulated creature called a 'spark' learn to seek light through reinforcement learning. The spark's brain has just... canonical: https://daily.dev/posts/a-visual-explanation-of-reinforcement-learning-y5sqxhdow twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: A visual explanation of reinforcement learning | daily.dev og:description: This interactive explainer lets you watch a simulated creature called a 'spark' learn to seek light through reinforcement learning. The spark's brain has just... og:url: https://daily.dev/posts/a-visual-explanation-of-reinforcement-learning-y5sqxhdow og:image: https://api.daily.dev/og/posts/Y5SqXhDOW.png og:image:alt: A visual explanation of reinforcement learning og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # A visual explanation of reinforcement learning **[Hacker News](https://daily.dev/sources/hn)** · 9 min read · 15 upvotes · 0 comments ## Summary This interactive explainer lets you watch a simulated creature called a 'spark' learn to seek light through reinforcement learning. The spark's brain has just four neurons and three tunable parameters: straight connections, crossed connections, and drive. You can wire it by hand with sliders, then watch an automated learning algorithm take over. After 50 tries it takes its first steps, after 500 it charges at the light, and by 2,000 tries it expertly tracks and follows it. The approach implements a 1992 REINFORCE algorithm variant by Ronald Williams, using weight perturbation similar to the evolution strategies OpenAI used in 2017 for Atari-playing AI. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the REINFORCE algorithm and how does it update parameters based on reward? REINFORCE, published by Ronald Williams in 1992, updates parameters by randomly perturbing them each trial, then nudging them in the direction of the perturbation if the resulting reward beats a running average, or the opposite direction if it performs worse. The size of the nudge scales with how much better or worse the trial performed compared to recent average reward. _daily.dev surfaces explainers like this for developers building intuition around reinforcement learning algorithms._ ### What is weight perturbation and how does it differ from Williams' original REINFORCE approach? Weight perturbation adds randomness directly to a neural network's weights and biases rather than to each neuron's output, which was Williams' original 1992 approach. This variant is similar to evolution strategies, a technique OpenAI used in 2017 to train AI models to play Atari video games. _Developers comparing RL training techniques can track variations like this through daily.dev._ ### How many neurons and parameters are needed for a simple creature to learn to seek light using reinforcement learning? Just four neurons, two acting as eyes and two controlling flippers, with only three tunable parameters: straight connection strength, crossed connection strength, and a drive value. Starting from random wiring, the creature takes its first steps after 50 trials and becomes an expert light-seeker after 2,000 trials. _daily.dev helps developers exploring minimal reinforcement learning models follow similar case studies._ ## Similar posts on daily.dev - [Simple circuits can create complex behaviors](https://daily.dev/posts/simple-circuits-can-create-complex-behaviors-wybaz0cl0) · Hacker News · 0 upvotes · 0 comments - [A neural blueprint for human-like intelligence in soft robots](https://daily.dev/posts/a-neural-blueprint-for-human-like-intelligence-in-soft-robots-7efw2mkgi) · MIT News · 0 upvotes · 0 comments --- Tags: [#neural-networks](https://daily.dev/tags/neural-networks), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning) [View this post on daily.dev](https://daily.dev/posts/a-visual-explanation-of-reinforcement-learning-y5sqxhdow) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"A visual explanation of reinforcement learning","url":"https://daily.dev/posts/a-visual-explanation-of-reinforcement-learning-y5sqxhdow","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/a-visual-explanation-of-reinforcement-learning-y5sqxhdow"},"datePublished":"2026-11-14T19:04:44.118Z","dateModified":"2026-11-14T19:15:39.026Z","description":"This interactive explainer lets you watch a simulated creature called a 'spark' learn to seek light through reinforcement learning. 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