import{s as O,n as W,o as X}from"../chunks/scheduler.37c15a92.js";import{S as Y,i as Z,g as l,s as o,r as E,A as J,h as r,f as n,c as i,j as q,u as A,x as v,k as B,y as Q,a,v as S,d as j,t as I,w as z}from"../chunks/index.7cb9c9b8.js";import{H as F,E as V}from"../chunks/EditOnGithub.858acfec.js";function tt(G){let s,y,w,_,m,b,u,K=`<strong>Congrats on finishing this unit</strong>! There was a lot of information.
And congrats on finishing the tutorial. You’ve just coded your first Deep Reinforcement Learning agent from scratch using PyTorch and shared it on the Hub 🥳.`,x,f,N=`Don’t hesitate to iterate on this unit <strong>by improving the implementation for more complex environments</strong> (for instance, what about changing the network to a Convolutional Neural Network to handle
frames as observation)?`,L,p,U=`In the next unit, <strong>we’re going to learn more about Unity MLAgents</strong>, by training agents in Unity environments. This way, you will be ready to participate in the <strong>AI vs AI challenges where you’ll train your agents
to compete against other agents in a snowball fight and a soccer game.</strong>`,T,h,D="Sound fun? See you next time!",C,c,R='Finally, we would love <strong>to hear what you think of the course and how we can improve it</strong>. If you have some feedback then please 👉  <a href="https://forms.gle/BzKXWzLAGZESGNaE9" rel="nofollow">fill this form</a>',P,g,H,d,M,$,k;return m=new F({props:{title:"Conclusion",local:"conclusion",headingTag:"h1"}}),g=new F({props:{title:"Keep Learning, stay awesome 🤗",local:"keep-learning-stay-awesome-",headingTag:"h3"}}),d=new V({props:{source:"https://github.com/huggingface/deep-rl-class/blob/main/units/en/unit4/conclusion.mdx"}}),{c(){s=l("meta"),y=o(),w=l("p"),_=o(),E(m.$$.fragment),b=o(),u=l("p"),u.innerHTML=K,x=o(),f=l("p"),f.innerHTML=N,L=o(),p=l("p"),p.innerHTML=U,T=o(),h=l("p"),h.textContent=D,C=o(),c=l("p"),c.innerHTML=R,P=o(),E(g.$$.fragment),H=o(),E(d.$$.fragment),M=o(),$=l("p"),this.h()},l(t){const e=J("svelte-u9bgzb",document.head);s=r(e,"META",{name:!0,content:!0}),e.forEach(n),y=i(t),w=r(t,"P",{}),q(w).forEach(n),_=i(t),A(m.$$.fragment,t),b=i(t),u=r(t,"P",{"data-svelte-h":!0}),v(u)!=="svelte-jb70tl"&&(u.innerHTML=K),x=i(t),f=r(t,"P",{"data-svelte-h":!0}),v(f)!=="svelte-h09rnt"&&(f.innerHTML=N),L=i(t),p=r(t,"P",{"data-svelte-h":!0}),v(p)!=="svelte-st4ijf"&&(p.innerHTML=U),T=i(t),h=r(t,"P",{"data-svelte-h":!0}),v(h)!=="svelte-2auufk"&&(h.textContent=D),C=i(t),c=r(t,"P",{"data-svelte-h":!0}),v(c)!=="svelte-ol1ouv"&&(c.innerHTML=R),P=i(t),A(g.$$.fragment,t),H=i(t),A(d.$$.fragment,t),M=i(t),$=r(t,"P",{}),q($).forEach(n),this.h()},h(){B(s,"name","hf:doc:metadata"),B(s,"content",et)},m(t,e){Q(document.head,s),a(t,y,e),a(t,w,e),a(t,_,e),S(m,t,e),a(t,b,e),a(t,u,e),a(t,x,e),a(t,f,e),a(t,L,e),a(t,p,e),a(t,T,e),a(t,h,e),a(t,C,e),a(t,c,e),a(t,P,e),S(g,t,e),a(t,H,e),S(d,t,e),a(t,M,e),a(t,$,e),k=!0},p:W,i(t){k||(j(m.$$.fragment,t),j(g.$$.fragment,t),j(d.$$.fragment,t),k=!0)},o(t){I(m.$$.fragment,t),I(g.$$.fragment,t),I(d.$$.fragment,t),k=!1},d(t){t&&(n(y),n(w),n(_),n(b),n(u),n(x),n(f),n(L),n(p),n(T),n(h),n(C),n(c),n(P),n(H),n(M),n($)),n(s),z(m,t),z(g,t),z(d,t)}}}const et='{"title":"Conclusion","local":"conclusion","sections":[{"title":"Keep Learning, stay awesome 🤗","local":"keep-learning-stay-awesome-","sections":[],"depth":3}],"depth":1}';function nt(G){return X(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class st extends Y{constructor(s){super(),Z(this,s,nt,tt,O,{})}}export{st as component};
