import{s as N,n as Q,o as V}from"../chunks/scheduler.37c15a92.js";import{S as W,i as X,g,s as l,r as _,A as Y,h as $,f as i,c as a,j as J,u as w,x as O,k as K,y as Z,a as n,v as P,d as x,t as L,w as T}from"../chunks/index.7cb9c9b8.js";import{H as U,E as tt}from"../chunks/EditOnGithub.858acfec.js";function et(k){let o,v,y,M,r,H,m,D="These are <strong>optional readings</strong> if you want to go deeper.",R,s,E,p,q='<li><a href="https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html" rel="nofollow">Part 3: Intro to Policy Optimization - Spinning Up documentation</a></li>',G,f,z,d,B='<li><a href="https://johnwlambert.github.io/policy-gradients/" rel="nofollow">https://johnwlambert.github.io/policy-gradients/</a></li> <li><a href="https://jonathan-hui.medium.com/rl-policy-gradients-explained-9b13b688b146" rel="nofollow">RL - Policy Gradient Explained</a></li> <li><a href="http://incompleteideas.net/book/RLbook2020.pdf" rel="nofollow">Chapter 13, Policy Gradient Methods;  Reinforcement Learning, an introduction by Richard Sutton and Andrew G. Barto</a></li>',C,u,I,c,F='<li><a href="https://github.com/pytorch/examples/blob/main/reinforcement_learning/reinforce.py" rel="nofollow">PyTorch Reinforce implementation</a></li> <li><a href="https://github.com/MrSyee/pg-is-all-you-need" rel="nofollow">Implementations from DDPG to PPO</a></li>',S,h,j,b,A;return r=new U({props:{title:"Additional Readings",local:"additional-readings",headingTag:"h1"}}),s=new U({props:{title:"Introduction to Policy Optimization",local:"introduction-to-policy-optimization",headingTag:"h2"}}),f=new U({props:{title:"Policy Gradient",local:"policy-gradient",headingTag:"h2"}}),u=new U({props:{title:"Implementation",local:"implementation",headingTag:"h2"}}),h=new tt({props:{source:"https://github.com/huggingface/deep-rl-class/blob/main/units/en/unit4/additional-readings.mdx"}}),{c(){o=g("meta"),v=l(),y=g("p"),M=l(),_(r.$$.fragment),H=l(),m=g("p"),m.innerHTML=D,R=l(),_(s.$$.fragment),E=l(),p=g("ul"),p.innerHTML=q,G=l(),_(f.$$.fragment),z=l(),d=g("ul"),d.innerHTML=B,C=l(),_(u.$$.fragment),I=l(),c=g("ul"),c.innerHTML=F,S=l(),_(h.$$.fragment),j=l(),b=g("p"),this.h()},l(t){const e=Y("svelte-u9bgzb",document.head);o=$(e,"META",{name:!0,content:!0}),e.forEach(i),v=a(t),y=$(t,"P",{}),J(y).forEach(i),M=a(t),w(r.$$.fragment,t),H=a(t),m=$(t,"P",{"data-svelte-h":!0}),O(m)!=="svelte-19yxmer"&&(m.innerHTML=D),R=a(t),w(s.$$.fragment,t),E=a(t),p=$(t,"UL",{"data-svelte-h":!0}),O(p)!=="svelte-6l3iyx"&&(p.innerHTML=q),G=a(t),w(f.$$.fragment,t),z=a(t),d=$(t,"UL",{"data-svelte-h":!0}),O(d)!=="svelte-11bjwl7"&&(d.innerHTML=B),C=a(t),w(u.$$.fragment,t),I=a(t),c=$(t,"UL",{"data-svelte-h":!0}),O(c)!=="svelte-lgx1nu"&&(c.innerHTML=F),S=a(t),w(h.$$.fragment,t),j=a(t),b=$(t,"P",{}),J(b).forEach(i),this.h()},h(){K(o,"name","hf:doc:metadata"),K(o,"content",it)},m(t,e){Z(document.head,o),n(t,v,e),n(t,y,e),n(t,M,e),P(r,t,e),n(t,H,e),n(t,m,e),n(t,R,e),P(s,t,e),n(t,E,e),n(t,p,e),n(t,G,e),P(f,t,e),n(t,z,e),n(t,d,e),n(t,C,e),P(u,t,e),n(t,I,e),n(t,c,e),n(t,S,e),P(h,t,e),n(t,j,e),n(t,b,e),A=!0},p:Q,i(t){A||(x(r.$$.fragment,t),x(s.$$.fragment,t),x(f.$$.fragment,t),x(u.$$.fragment,t),x(h.$$.fragment,t),A=!0)},o(t){L(r.$$.fragment,t),L(s.$$.fragment,t),L(f.$$.fragment,t),L(u.$$.fragment,t),L(h.$$.fragment,t),A=!1},d(t){t&&(i(v),i(y),i(M),i(H),i(m),i(R),i(E),i(p),i(G),i(z),i(d),i(C),i(I),i(c),i(S),i(j),i(b)),i(o),T(r,t),T(s,t),T(f,t),T(u,t),T(h,t)}}}const it='{"title":"Additional Readings","local":"additional-readings","sections":[{"title":"Introduction to Policy Optimization","local":"introduction-to-policy-optimization","sections":[],"depth":2},{"title":"Policy Gradient","local":"policy-gradient","sections":[],"depth":2},{"title":"Implementation","local":"implementation","sections":[],"depth":2}],"depth":1}';function nt(k){return V(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class rt extends W{constructor(o){super(),X(this,o,nt,et,N,{})}}export{rt as component};
