import{s as N,f as R,n as Q,o as V}from"../chunks/scheduler.37c15a92.js";import{S as W,i as X,g as l,s as o,r as q,A as Y,h as r,f as n,c as i,j as D,u as U,x as C,k as v,y as Z,a,v as j,d as B,t as J,w as K}from"../chunks/index.7cb9c9b8.js";import{H as tt,E as et}from"../chunks/EditOnGithub.858acfec.js";function nt(G){let s,w,_,P,m,$,c,I,b,u,O='In this second part of Unit 8, we’ll get deeper into PPO optimization by using <a href="https://samplefactory.dev/" rel="nofollow">Sample-Factory</a>, an <strong>asynchronous implementation of the PPO algorithm</strong>, to train our agent to play <a href="https://vizdoom.cs.put.edu.pl/" rel="nofollow">vizdoom</a> (an open source version of Doom).',x,p,k="In the notebook, <strong>you’ll train your agent to play the Health Gathering level</strong>, where the agent must collect health packs to avoid dying. After that, you can <strong>train your agent to play more complex levels, such as Deathmatch</strong>.",H,h,z,M,f,A="Sound exciting? Let’s get started! 🚀",L,g,F='The hands-on is made by <a href="https://twitter.com/edwardbeeching" rel="nofollow">Edward Beeching</a>, a Machine Learning Research Scientist at Hugging Face. He worked on Godot Reinforcement Learning Agents, an open-source interface for developing environments and agents in the Godot Game Engine.',E,d,T,y,S;return m=new tt({props:{title:"Introduction to PPO with Sample-Factory",local:"introduction-to-ppo-with-sample-factory",headingTag:"h1"}}),d=new et({props:{source:"https://github.com/huggingface/deep-rl-class/blob/main/units/en/unit8/introduction-sf.mdx"}}),{c(){s=l("meta"),w=o(),_=l("p"),P=o(),q(m.$$.fragment),$=o(),c=l("img"),b=o(),u=l("p"),u.innerHTML=O,x=o(),p=l("p"),p.innerHTML=k,H=o(),h=l("img"),M=o(),f=l("p"),f.textContent=A,L=o(),g=l("p"),g.innerHTML=F,E=o(),q(d.$$.fragment),T=o(),y=l("p"),this.h()},l(t){const e=Y("svelte-u9bgzb",document.head);s=r(e,"META",{name:!0,content:!0}),e.forEach(n),w=i(t),_=r(t,"P",{}),D(_).forEach(n),P=i(t),U(m.$$.fragment,t),$=i(t),c=r(t,"IMG",{src:!0,alt:!0}),b=i(t),u=r(t,"P",{"data-svelte-h":!0}),C(u)!=="svelte-h9ne9b"&&(u.innerHTML=O),x=i(t),p=r(t,"P",{"data-svelte-h":!0}),C(p)!=="svelte-o5evae"&&(p.innerHTML=k),H=i(t),h=r(t,"IMG",{src:!0,alt:!0}),M=i(t),f=r(t,"P",{"data-svelte-h":!0}),C(f)!=="svelte-1i7vabt"&&(f.textContent=A),L=i(t),g=r(t,"P",{"data-svelte-h":!0}),C(g)!=="svelte-dm6yco"&&(g.innerHTML=F),E=i(t),U(d.$$.fragment,t),T=i(t),y=r(t,"P",{}),D(y).forEach(n),this.h()},h(){v(s,"name","hf:doc:metadata"),v(s,"content",at),R(c.src,I="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit9/thumbnail2.png")||v(c,"src",I),v(c,"alt","thumbnail"),R(h.src,z="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit10/environments.png")||v(h,"src",z),v(h,"alt","Environment")},m(t,e){Z(document.head,s),a(t,w,e),a(t,_,e),a(t,P,e),j(m,t,e),a(t,$,e),a(t,c,e),a(t,b,e),a(t,u,e),a(t,x,e),a(t,p,e),a(t,H,e),a(t,h,e),a(t,M,e),a(t,f,e),a(t,L,e),a(t,g,e),a(t,E,e),j(d,t,e),a(t,T,e),a(t,y,e),S=!0},p:Q,i(t){S||(B(m.$$.fragment,t),B(d.$$.fragment,t),S=!0)},o(t){J(m.$$.fragment,t),J(d.$$.fragment,t),S=!1},d(t){t&&(n(w),n(_),n(P),n($),n(c),n(b),n(u),n(x),n(p),n(H),n(h),n(M),n(f),n(L),n(g),n(E),n(T),n(y)),n(s),K(m,t),K(d,t)}}}const at='{"title":"Introduction to PPO with Sample-Factory","local":"introduction-to-ppo-with-sample-factory","sections":[],"depth":1}';function ot(G){return V(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class rt extends W{constructor(s){super(),X(this,s,ot,nt,N,{})}}export{rt as component};
