import{s as oe,f as z,n as ue,o as me}from"../chunks/scheduler.37c15a92.js";import{S as pe,i as ce,g as l,s as a,r as ne,A as fe,h as r,f as n,c as s,j as B,u as ie,x as c,k as u,y as ge,a as i,v as ae,d as se,t as le,w as re}from"../chunks/index.7cb9c9b8.js";import{H as he,E as de}from"../chunks/EditOnGithub.858acfec.js";function ve(F){let m,x,T,P,f,w,p,V,C,g,W="Welcome to the most fascinating topic in Artificial Intelligence: <strong>Deep Reinforcement Learning.</strong>",D,h,J="Deep RL is a type of Machine Learning where an agent learns <strong>how to behave</strong> in an environment <strong>by performing actions</strong> and <strong>seeing the results.</strong>",R,d,K="In this first unit, <strong>you’ll learn the foundations of Deep Reinforcement Learning.</strong>",E,v,N='Then, you’ll <strong>train your Deep Reinforcement Learning agent, a lunar lander to land correctly on the Moon</strong> using <a href="https://stable-baselines3.readthedocs.io/en/master/">Stable-Baselines3 </a>, a Deep Reinforcement Learning library.',I,_,Q,q,L,X="And finally, you’ll <strong>upload this trained agent to the Hugging Face Hub 🤗, a free, open platform where people can share ML models, datasets, and demos.</strong>",k,y,Y="It’s essential <strong>to master these elements</strong> before diving into implementing Deep Reinforcement Learning agents. The goal of this chapter is to give you solid foundations.",A,b,Z="After this unit, in a bonus unit, you’ll be <strong>able to train Huggy the Dog 🐶 to fetch the stick and play with him 🤗</strong>.",S,o,ee,G,M,te="So let’s get started! 🚀",j,H,O,$,U;return f=new he({props:{title:"Introduction to Deep Reinforcement Learning",local:"introduction-to-deep-reinforcement-learning",headingTag:"h1"}}),H=new de({props:{source:"https://github.com/huggingface/deep-rl-class/blob/main/units/en/unit1/introduction.mdx"}}),{c(){m=l("meta"),x=a(),T=l("p"),P=a(),ne(f.$$.fragment),w=a(),p=l("img"),C=a(),g=l("p"),g.innerHTML=W,D=a(),h=l("p"),h.innerHTML=J,R=a(),d=l("p"),d.innerHTML=K,E=a(),v=l("p"),v.innerHTML=N,I=a(),_=l("img"),q=a(),L=l("p"),L.innerHTML=X,k=a(),y=l("p"),y.innerHTML=Y,A=a(),b=l("p"),b.innerHTML=Z,S=a(),o=l("video"),G=a(),M=l("p"),M.textContent=te,j=a(),ne(H.$$.fragment),O=a(),$=l("p"),this.h()},l(e){const t=fe("svelte-u9bgzb",document.head);m=r(t,"META",{name:!0,content:!0}),t.forEach(n),x=s(e),T=r(e,"P",{}),B(T).forEach(n),P=s(e),ie(f.$$.fragment,e),w=s(e),p=r(e,"IMG",{src:!0,alt:!0,width:!0}),C=s(e),g=r(e,"P",{"data-svelte-h":!0}),c(g)!=="svelte-1n1ak13"&&(g.innerHTML=W),D=s(e),h=r(e,"P",{"data-svelte-h":!0}),c(h)!=="svelte-18qladf"&&(h.innerHTML=J),R=s(e),d=r(e,"P",{"data-svelte-h":!0}),c(d)!=="svelte-2548rb"&&(d.innerHTML=K),E=s(e),v=r(e,"P",{"data-svelte-h":!0}),c(v)!=="svelte-xcqpqx"&&(v.innerHTML=N),I=s(e),_=r(e,"IMG",{src:!0,alt:!0}),q=s(e),L=r(e,"P",{"data-svelte-h":!0}),c(L)!=="svelte-qeetmg"&&(L.innerHTML=X),k=s(e),y=r(e,"P",{"data-svelte-h":!0}),c(y)!=="svelte-ergnfr"&&(y.innerHTML=Y),A=s(e),b=r(e,"P",{"data-svelte-h":!0}),c(b)!=="svelte-1akna90"&&(b.innerHTML=Z),S=s(e),o=r(e,"VIDEO",{src:!0,type:!0,mute:!0}),B(o).forEach(n),G=s(e),M=r(e,"P",{"data-svelte-h":!0}),c(M)!=="svelte-vkoquy"&&(M.textContent=te),j=s(e),ie(H.$$.fragment,e),O=s(e),$=r(e,"P",{}),B($).forEach(n),this.h()},h(){u(m,"name","hf:doc:metadata"),u(m,"content",_e),z(p.src,V="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/thumbnail.jpg")||u(p,"src",V),u(p,"alt","Unit 1 thumbnail"),u(p,"width","100%"),z(_.src,Q="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/lunarLander.gif")||u(_,"src",Q),u(_,"alt","LunarLander"),z(o.src,ee="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit0/huggy.mp4")||u(o,"src",ee),u(o,"type","video/mp4"),o.controls=!0,o.autoplay=!0,o.loop=!0,u(o,"mute","")},m(e,t){ge(document.head,m),i(e,x,t),i(e,T,t),i(e,P,t),ae(f,e,t),i(e,w,t),i(e,p,t),i(e,C,t),i(e,g,t),i(e,D,t),i(e,h,t),i(e,R,t),i(e,d,t),i(e,E,t),i(e,v,t),i(e,I,t),i(e,_,t),i(e,q,t),i(e,L,t),i(e,k,t),i(e,y,t),i(e,A,t),i(e,b,t),i(e,S,t),i(e,o,t),i(e,G,t),i(e,M,t),i(e,j,t),ae(H,e,t),i(e,O,t),i(e,$,t),U=!0},p:ue,i(e){U||(se(f.$$.fragment,e),se(H.$$.fragment,e),U=!0)},o(e){le(f.$$.fragment,e),le(H.$$.fragment,e),U=!1},d(e){e&&(n(x),n(T),n(P),n(w),n(p),n(C),n(g),n(D),n(h),n(R),n(d),n(E),n(v),n(I),n(_),n(q),n(L),n(k),n(y),n(A),n(b),n(S),n(o),n(G),n(M),n(j),n(O),n($)),n(m),re(f,e),re(H,e)}}}const _e='{"title":"Introduction to Deep Reinforcement Learning","local":"introduction-to-deep-reinforcement-learning","sections":[],"depth":1}';function Le(F){return me(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class He extends pe{constructor(m){super(),ce(this,m,Le,ve,oe,{})}}export{He as component};
