{"id":122823,"date":"2026-11-05T19:48:57","date_gmt":"2026-11-05T11:48:57","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/122823.html"},"modified":"2026-11-05T19:48:57","modified_gmt":"2026-11-05T11:48:57","slug":"%e4%b8%96%e7%95%8c%e6%a8%a1%e5%9e%8b%e7%9a%84%e3%80%8c%e5%ba%94%e8%af%95%e6%95%99%e8%82%b2%e3%80%8d%e7%bb%88%e7%bb%93%e4%ba%86%ef%bc%9fworld2vec-%e5%a6%82%e4%bd%95%e7%94%a8%e5%8d%81%e5%88%86%e4%b9%8b","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/122823.html","title":{"rendered":"\u4e16\u754c\u6a21\u578b\u7684\u300c\u5e94\u8bd5\u6559\u80b2\u300d\u7ec8\u7ed3\u4e86\uff1fworld2vec \u5982\u4f55\u7528\u5341\u5206\u4e4b\u4e00\u7684\u53c2\u6570\u771f\u6b63\u5b66\u61c2\u7269\u7406"},"content":{"rendered":"<p>&#x1f44b; Hi&#xff0c;\u6211\u64c5\u957f AI \u5927\u6a21\u578b\u5e94\u7528\u843d\u5730\u3001\u610f\u8bc6\u89e3\u7801\u4e0e AI \u5f00\u53d1\u5de5\u5177\u94fe \u3002 &#x1f4a1; \u521b\u4e1a\u8def\u4e0a&#xff0c;\u7528\u6280\u672f\u6362\u65f6\u95f4&#xff0c;\u4e00\u8d77\u628a AI \u53d8\u6210\u751f\u4ea7\u529b &#x1f680; &gt;<\/p>\n<hr \/>\n<h2>\u4e16\u754c\u6a21\u578b\u7684\u300c\u5e94\u8bd5\u6559\u80b2\u300d\u7ec8\u7ed3\u4e86&#xff1f;world2vec \u5982\u4f55\u7528\u5341\u5206\u4e4b\u4e00\u7684\u53c2\u6570\u771f\u6b63\u5b66\u61c2\u7269\u7406<\/h2>\n<p>\u60f3\u8c61\u8fd9\u6837\u4e00\u4e2a\u573a\u666f&#xff1a;\u4f60\u82b1\u4e86\u4e24\u5468&#xff0c;\u57fa\u4e8e\u4e16\u754c\u6a21\u578b\u8bad\u7ec3\u51fa\u4e00\u4e2a\u5f3a\u5316\u5b66\u4e60\u667a\u80fd\u4f53&#xff0c;\u89c6\u89c9\u63a7\u5236\u4efb\u52a1\u7684\u56de\u62a5\u66f2\u7ebf\u6f02\u4eae\u5f97\u80fd\u76f4\u63a5\u622a\u56fe\u53d1\u670b\u53cb\u5708\u3002\u4f60\u5174\u51b2\u51b2\u62ff\u7ed9\u5bfc\u5e08\u770b&#xff0c;\u5bf9\u65b9\u53ea\u95ee\u4e86\u4e00\u53e5&#xff1a;&#034;\u8ba9\u6a21\u578b\u5728\u8111\u5b50\u91cc\u5f80\u524d\u63a8\u6f14 500 \u6b65&#xff0c;\u5b83\u8fd8\u9075\u5b88\u7269\u7406\u89c4\u5f8b\u5417&#xff1f;&#034;\u4f60\u8dd1\u4e86\u4e00\u4e0b\u2014\u2014\u753b\u9762\u91cc\u7684\u6446\u6746\u5f00\u59cb\u60ac\u6d6e\u3001\u77ac\u79fb\u3001\u51ed\u7a7a\u6d88\u5931\u3002<\/p>\n<p>\u6a21\u578b\u786e\u5b9e\u4f1a&#034;\u505a\u9898&#034;&#xff0c;\u4f46\u5b83\u6839\u672c\u6ca1&#034;\u61c2&#034;\u8fd9\u4e2a\u4e16\u754c\u3002\u8fd9\u4e0d\u662f\u4f60\u4e00\u4e2a\u4eba\u7684\u5751&#xff0c;\u800c\u662f\u6574\u4e2a\u9886\u57df\u7684\u9690\u75db\u3002\u6700\u8fd1\u51fa\u73b0\u7684 world2vec&#xff0c;\u5c31\u662f\u51b2\u7740\u8fd9\u4e2a\u75db\u70b9\u6765\u7684\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/11\/20261105114853-6aec6d2587fa4.jpg\" alt=\"Abstract conceptual art: a translucent glass-like \" \/><\/p>\n<h3>30 \u79d2\u7ed3\u8bba<\/h3>\n<ul>\n<li>\u6838\u5fc3\u5224\u65ad&#xff1a;world2vec \u8bc1\u660e\u4e86\u4e00\u4ef6\u4e8b\u2014\u2014\u4e0d\u9700\u8981\u4efb\u4f55\u5956\u52b1\u4fe1\u53f7&#xff0c;\u53ea\u4ece\u89c2\u6d4b\u548c\u52a8\u4f5c\u5e8f\u5217&#xff0c;\u5c31\u80fd\u5b66\u51fa\u5fe0\u5b9e\u8fd8\u539f\u771f\u5b9e\u7cfb\u7edf\u52a8\u529b\u5b66\u7684\u4e16\u754c\u6a21\u578b&#xff0c;\u4e14\u53c2\u6570\u91cf\u6bd4 DreamerV3\u3001TD-MPC2\u3001LeWM \u5c11\u4e86\u81f3\u5c11\u4e00\u4e2a\u6570\u91cf\u7ea7\u3002<\/li>\n<li>\u9002\u5408\u8c01&#xff1a;\u505a\u5177\u8eab\u667a\u80fd\u3001\u673a\u5668\u4eba\u3001\u6a21\u578b\u9884\u6d4b\u63a7\u5236\u65b9\u5411\u7684\u5b66\u751f&#xff1b;\u60f3\u7ed9\u4f5c\u54c1\u96c6\u52a0\u4e00\u4e2a&#034;\u81ea\u76d1\u7763\u4e16\u754c\u6a21\u578b&#034;\u9879\u76ee\u7684\u8f6c\u884c\u8005\u3002\u524d\u7f6e\u77e5\u8bc6\u8981\u6c42\u4e0d\u9ad8&#xff1a;PyTorch \u57fa\u7840 &#043; \u7406\u89e3\u5f3a\u5316\u5b66\u4e60\u91cc\u72b6\u6001\/\u52a8\u4f5c\/\u5956\u52b1\u7684\u6982\u5ff5\u5373\u53ef\u3002<\/li>\n<li>\u4e0d\u9002\u5408\u8c01&#xff1a;\u53ea\u60f3\u5728\u5355\u4e00\u4efb\u52a1\u4e0a\u5feb\u901f\u5237\u5206\u7684\u4eba&#xff08;\u6210\u719f\u7684\u5956\u52b1\u9a71\u52a8\u65b9\u6848\u66f4\u7701\u4e8b&#xff09;&#xff1b;\u9700\u8981\u7acb\u523b\u4e0a\u751f\u4ea7\u73af\u5883\u7684\u4eba&#xff08;\u8fd9\u662f\u524d\u6cbf\u7814\u7a76&#xff0c;\u4e0d\u662f\u5f00\u7bb1\u5373\u7528\u7684\u6846\u67b6&#xff09;\u3002<\/li>\n<\/ul>\n<h3>\u5173\u952e\u8bc1\u636e<\/h3>\n<li>\u5c0f\u53c2\u6570&#xff0c;\u5927\u6027\u80fd&#xff1a;\u7528\u6bd4\u4e3b\u6d41\u4e16\u754c\u6a21\u578b\u5c11\u81f3\u5c11 10 \u500d\u7684\u53c2\u6570&#xff0c;\u5728\u50cf\u7d20\u8f93\u5165\u7684\u8fde\u7eed\u63a7\u5236\u4efb\u52a1\u4e0a\u505a\u5230\u4e86\u5f53\u524d\u6700\u4f73\u6c34\u5e73\u3002<\/li>\n<li>\u8bad\u7ec3\u5b8c\u5168\u6446\u8131\u5956\u52b1&#xff1a;\u53ea\u4f9d\u8d56\u89c2\u6d4b\u4e0e\u52a8\u4f5c&#xff0c;\u610f\u5473\u7740\u540c\u4e00\u5957\u52a8\u529b\u5b66\u53ef\u4ee5\u670d\u52a1\u4efb\u610f\u4e0b\u6e38\u4efb\u52a1&#xff0c;\u800c\u4e0d\u662f\u4e3a\u4e00\u4e2a\u4efb\u52a1\u5b9a\u5236\u3002<\/li>\n<li>\u957f\u65f6\u7a0b\u4fdd\u771f&#xff1a;\u5b66\u5230\u7684\u6f5c\u7a7a\u95f4\u52a8\u529b\u5b66\u80fd\u5728\u5f88\u957f\u7684\u9884\u6d4b\u8de8\u5ea6\u4e0a\u8fd8\u539f\u771f\u5b9e\u7cfb\u7edf\u7684\u6f14\u5316&#xff0c;\u800c\u4e0d\u662f\u53ea\u5728\u8bad\u7ec3\u5206\u5e03\u9644\u8fd1&#034;\u770b\u8d77\u6765\u50cf&#034;\u3002<\/li>\n<li>\u80fd\u8fa8\u8bc6\u771f\u5b9e\u7cfb\u7edf&#xff1a;\u5728\u7531 71 \u4e2a\u5438\u5f15\u5b50\u7ec4\u6210\u7684\u57fa\u51c6\u4e0a\u5b8c\u6210\u4e86\u7cbe\u786e\u7684\u7cfb\u7edf\u8fa8\u8bc6&#xff08;system identification&#xff09;\u2014\u2014\u8fd9\u662f&#034;\u4e16\u754c\u6a21\u578b\u53ef\u7528\u4e8e\u79d1\u5b66\u53d1\u73b0&#034;\u7684\u76f4\u63a5\u8bc1\u636e\u3002<\/li>\n<h3>\u5c55\u5f00\u8bf4\u660e<\/h3>\n<h4>\u95ee\u9898\u6839\u6e90&#xff1a;\u5956\u52b1\u662f\u4e2a\u504f\u79d1\u7684\u8001\u5e08<\/h4>\n<p>\u4f20\u7edf\u4e16\u754c\u6a21\u578b&#xff08;\u5982 Dreamer \u7cfb\u5217&#xff09;\u7684\u6f5c\u7a7a\u95f4\u662f\u56f4\u7740\u5956\u52b1\u8f6c\u7684&#xff1a;\u53ea\u8981\u67d0\u4e2a\u7279\u5f81\u5bf9\u9884\u6d4b\u56de\u62a5\u6ca1\u7528&#xff0c;\u7f16\u7801\u5668\u5c31\u6709\u5145\u5206\u7406\u7531\u628a\u5b83\u6254\u6389\u3002\u7ed3\u679c\u662f&#xff0c;\u6f5c\u52a8\u529b\u5b66\u53ea\u9700\u5728&#034;\u5bf9\u51b3\u7b56\u6709\u7528&#034;\u7684\u7ef4\u5ea6\u4e0a\u6b63\u786e&#xff0c;\u5176\u4f59\u90e8\u5206\u53ef\u4ee5\u4efb\u610f\u6f02\u79fb\u3002\u62ff\u9ad8\u5206\u548c\u61c2\u7269\u7406&#xff0c;\u4ece\u6765\u90fd\u662f\u4e24\u56de\u4e8b\u3002<\/p>\n<h4>world2vec \u7684\u601d\u8def&#xff1a;\u5728\u6f5c\u7a7a\u95f4\u5bf9\u9f50&#034;\u672a\u6765&#034;<\/h4>\n<p>\u540d\u5b57\u8ba9\u4eba\u8054\u60f3\u5230 word2vec&#xff1a;\u5f53\u5e74 word2vec \u9760&#034;\u7528\u8bcd\u9884\u6d4b\u4e0a\u4e0b\u6587&#034;\u5b66\u5230\u8bcd\u7684\u5411\u91cf\u8868\u793a&#xff1b;world2vec \u7c7b\u4f3c&#xff0c;\u9760&#034;\u7528\u5f53\u524d\u72b6\u6001\u548c\u52a8\u4f5c\u9884\u6d4b\u672a\u6765\u7684\u6f5c\u72b6\u6001&#034;\u6765\u5b66\u72b6\u6001\u7684\u8868\u793a\u3002\u673a\u5236\u53ef\u4ee5\u62c6\u6210\u4e09\u6b65&#xff1a;<\/p>\n<li>\u7f16\u7801\u5668\u628a\u89c2\u6d4b\u538b\u7f29\u6210\u6f5c\u72b6\u6001 z_t&#xff1b;<\/li>\n<li>\u8f6c\u79fb\u6a21\u578b\u5b66\u4e60 z_{t&#043;1} &#061; f(z_t, a_t)&#xff1b;<\/li>\n<li>\u8bad\u7ec3\u76ee\u6807\u662f\u8ba9\u9884\u6d4b\u7684 z_{t&#043;1} \u5bf9\u9f50\u771f\u5b9e\u4e0b\u4e00\u65f6\u523b\u89c2\u6d4b\u7684\u7f16\u7801\u2014\u2014\u5168\u90e8\u53d1\u751f\u5728\u6f5c\u7a7a\u95f4&#xff0c;\u4e0d\u91cd\u5efa\u50cf\u7d20\u3002<\/li>\n<p>\u4e0d\u91cd\u5efa\u50cf\u7d20\u662f\u5173\u952e&#xff1a;\u50cf\u7d20\u91cc\u585e\u6ee1\u4e86\u4e0e\u52a8\u529b\u5b66\u65e0\u5173\u7684\u7ec6\u8282&#xff08;\u80cc\u666f\u7eb9\u7406\u3001\u5149\u5f71\u53d8\u5316&#xff09;&#xff0c;\u9010\u50cf\u7d20\u9884\u6d4b\u65e2\u6d6a\u8d39\u7b97\u529b\u53c8\u5e26\u504f\u5b66\u4e60\u76ee\u6807\u3002\u8fd9\u547c\u5e94\u4e86 JEPA \u5f0f&#034;\u5728\u8868\u793a\u7a7a\u95f4\u505a\u9884\u6d4b&#034;\u7684\u601d\u60f3&#xff0c;\u5982\u4eca\u88ab\u624e\u5b9e\u5730\u642c\u8fdb\u4e86\u63a7\u5236\u9886\u57df\u3002<\/p>\n<p>\u4e00\u4e2a\u53ef\u4ee5\u4eca\u5929\u8dd1\u8d77\u6765\u7684\u6700\u5c0f\u5b9e\u73b0&#xff08;\u6570\u636e\u7528 Gymnasium \u7684 Pendulum-v1 \u79bb\u7ebf\u6536\u96c6&#xff09;&#xff1a;<\/p>\n<p><span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">,<\/span> torch<span class=\"token punctuation\">.<\/span>nn <span class=\"token keyword\">as<\/span> nn<\/p>\n<p><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">LatentDynamics<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> obs_dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">3<\/span><span class=\"token punctuation\">,<\/span> act_dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> z_dim<span class=\"token operator\">&#061;<\/span><span class=\"token number\">32<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>__init__<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>encoder <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span>obs_dim<span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> nn<span class=\"token punctuation\">.<\/span>ReLU<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> z_dim<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>transition <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>Sequential<span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span>z_dim <span class=\"token operator\">&#043;<\/span> act_dim<span class=\"token punctuation\">,<\/span> <span class=\"token number\">64<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> nn<span class=\"token punctuation\">.<\/span>ReLU<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> nn<span class=\"token punctuation\">.<\/span>Linear<span class=\"token punctuation\">(<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> z_dim<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">def<\/span> <span class=\"token function\">forward<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> obs<span class=\"token punctuation\">,<\/span> act<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        z <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>encoder<span class=\"token punctuation\">(<\/span>obs<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> z <span class=\"token operator\">&#043;<\/span> self<span class=\"token punctuation\">.<\/span>transition<span class=\"token punctuation\">(<\/span>torch<span class=\"token punctuation\">.<\/span>cat<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span>z<span class=\"token punctuation\">,<\/span> act<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span> dim<span class=\"token operator\">&#061;<\/span><span class=\"token operator\">&#8211;<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># \u6b8b\u5dee\u5f0f\u9884\u6d4b\u4e0b\u4e00\u6b65<\/span><\/p>\n<p>model <span class=\"token operator\">&#061;<\/span> LatentDynamics<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nopt <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>optim<span class=\"token punctuation\">.<\/span>Adam<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">.<\/span>parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> lr<span class=\"token operator\">&#061;<\/span><span class=\"token number\">3e-4<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">for<\/span> obs<span class=\"token punctuation\">,<\/span> act<span class=\"token punctuation\">,<\/span> obs_next <span class=\"token keyword\">in<\/span> loader<span class=\"token punctuation\">:<\/span>                  <span class=\"token comment\"># \u79bb\u7ebf\u6536\u96c6\u7684 (s, a, s&#039;) \u4e09\u5143\u7ec4<\/span><br \/>\n    <span class=\"token keyword\">with<\/span> torch<span class=\"token punctuation\">.<\/span>no_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        z_target <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">.<\/span>encoder<span class=\"token punctuation\">(<\/span>obs_next<span class=\"token punctuation\">)<\/span>         <span class=\"token comment\"># stop-grad&#xff0c;\u9632\u6b62\u8868\u793a\u574d\u584c<\/span><br \/>\n    loss <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>functional<span class=\"token punctuation\">.<\/span>mse_loss<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">(<\/span>obs<span class=\"token punctuation\">,<\/span> act<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">,<\/span> z_target<span class=\"token punctuation\">)<\/span><br \/>\n    opt<span class=\"token punctuation\">.<\/span>zero_grad<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span> loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">;<\/span> opt<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u6ce8\u610f\u90a3\u4e2a 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