import{s as O,n as V,o as B}from"../chunks/scheduler.37c15a92.js";import{S as F,i as J,g as l,s as o,r as j,A as K,h as r,f as a,c as s,j as q,u as G,x as v,k as D,y as N,a as i,v as Q,d as S,t as U,w as I}from"../chunks/index.7cb9c9b8.js";import{H as W,E as X}from"../chunks/EditOnGithub.858acfec.js";function Y(H){let n,b,g,x,p,_,m,z="This is a community-created glossary. Contributions are welcome!",w,u,E="<li><p><strong>Deep Q-Learning:</strong> A value-based deep reinforcement learning algorithm that uses a deep neural network to approximate Q-values for actions in a given state. The goal of Deep Q-learning is to find the optimal policy that maximizes the expected cumulative reward by learning the action-values.</p></li> <li><p><strong>Value-based methods:</strong> Reinforcement Learning methods that estimate a value function as an intermediate step towards finding an optimal policy.</p></li> <li><p><strong>Policy-based methods:</strong> Reinforcement Learning methods that directly learn to approximate the optimal policy without learning a value function. In practice they output a probability distribution over actions.</p> <p>The benefits of using policy-gradient methods over value-based methods include:</p> <ul><li>simplicity of integration: no need to store action values;</li> <li>ability to learn a stochastic policy: the agent explores the state space without always taking the same trajectory, and avoids the problem of perceptual aliasing;</li> <li>effectiveness in high-dimensional and continuous action spaces; and</li> <li>improved convergence properties.</li></ul></li> <li><p><strong>Policy Gradient:</strong> A subset of policy-based methods where the objective is to maximize the performance of a parameterized policy using gradient ascent. The goal of a policy-gradient is to control the probability distribution of actions by tuning the policy such that good actions (that maximize the return) are sampled more frequently in the future.</p></li> <li><p><strong>Monte Carlo Reinforce:</strong> A policy-gradient algorithm that uses an estimated return from an entire episode to update the policy parameter.</p></li>",$,c,k='If you want to improve the course, you can <a href="https://github.com/huggingface/deep-rl-class/pulls" rel="nofollow">open a Pull Request.</a>',C,h,A="This glossary was made possible thanks to:",T,d,R='<li><a href="https://github.com/dcarpintero" rel="nofollow">Diego Carpintero</a></li>',L,f,P,y,M;return p=new W({props:{title:"Glossary",local:"glossary",headingTag:"h1"}}),f=new X({props:{source:"https://github.com/huggingface/deep-rl-class/blob/main/units/en/unit4/glossary.mdx"}}),{c(){n=l("meta"),b=o(),g=l("p"),x=o(),j(p.$$.fragment),_=o(),m=l("p"),m.textContent=z,w=o(),u=l("ul"),u.innerHTML=E,$=o(),c=l("p"),c.innerHTML=k,C=o(),h=l("p"),h.textContent=A,T=o(),d=l("ul"),d.innerHTML=R,L=o(),j(f.$$.fragment),P=o(),y=l("p"),this.h()},l(e){const t=K("svelte-u9bgzb",document.head);n=r(t,"META",{name:!0,content:!0}),t.forEach(a),b=s(e),g=r(e,"P",{}),q(g).forEach(a),x=s(e),G(p.$$.fragment,e),_=s(e),m=r(e,"P",{"data-svelte-h":!0}),v(m)!=="svelte-1fj5fvd"&&(m.textContent=z),w=s(e),u=r(e,"UL",{"data-svelte-h":!0}),v(u)!=="svelte-18i5mkt"&&(u.innerHTML=E),$=s(e),c=r(e,"P",{"data-svelte-h":!0}),v(c)!=="svelte-1p0vyzq"&&(c.innerHTML=k),C=s(e),h=r(e,"P",{"data-svelte-h":!0}),v(h)!=="svelte-1abantd"&&(h.textContent=A),T=s(e),d=r(e,"UL",{"data-svelte-h":!0}),v(d)!=="svelte-18mimgc"&&(d.innerHTML=R),L=s(e),G(f.$$.fragment,e),P=s(e),y=r(e,"P",{}),q(y).forEach(a),this.h()},h(){D(n,"name","hf:doc:metadata"),D(n,"content",Z)},m(e,t){N(document.head,n),i(e,b,t),i(e,g,t),i(e,x,t),Q(p,e,t),i(e,_,t),i(e,m,t),i(e,w,t),i(e,u,t),i(e,$,t),i(e,c,t),i(e,C,t),i(e,h,t),i(e,T,t),i(e,d,t),i(e,L,t),Q(f,e,t),i(e,P,t),i(e,y,t),M=!0},p:V,i(e){M||(S(p.$$.fragment,e),S(f.$$.fragment,e),M=!0)},o(e){U(p.$$.fragment,e),U(f.$$.fragment,e),M=!1},d(e){e&&(a(b),a(g),a(x),a(_),a(m),a(w),a(u),a($),a(c),a(C),a(h),a(T),a(d),a(L),a(P),a(y)),a(n),I(p,e),I(f,e)}}}const Z='{"title":"Glossary","local":"glossary","sections":[],"depth":1}';function ee(H){return B(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ne extends F{constructor(n){super(),J(this,n,ee,Y,O,{})}}export{ne as component};
