\u8f6c\u8f7d\uff1a\u5c0f\u7ea2\u4e66 AI\u4ea7\u54c1\u8d75\u54e5<\/p>\n\n\n\n
\u524d\u8a00 \ud83d\udd16<\/strong><\/p>\n\n\n\n \u5728\u5f00\u59cb\u4eca\u5929\u7684\u8bdd\u9898\u524d\uff0c\u8bf7\u4f60\u56de\u60f3\u4e00\u4e0b\uff0c\u4f60\u662f\u5982\u4f55\u5b66\u4f1a\u4e00\u9879\u65b0\u6280\u80fd\u7684\uff1f\u6bd4\u5982\u9a91\u81ea\u884c\u8f66\u3001\u6253\u6e38\u620f\uff0c\u6216\u8005\u505a\u4e00\u9053\u65b0\u83dc\u3002<\/p>\n\n\n\n \u8fd9\u4e2a\u8fc7\u7a0b\uff0c\u901a\u5e38\u4e0d\u662f\u9760\u770b\u4e66\u6216\u8005\u542c\u8bfe\uff08\u76d1\u7763\u5b66\u4e60\uff09\uff0c\u4e5f\u4e0d\u662f\u9760\u81ea\u5df1\u7422\u78e8 \u201c\u8fd9\u5806\u96f6\u4ef6\u91cc\u6709\u4ec0\u4e48\u89c4\u5f8b\u201d\uff08\u65e0\u76d1\u7763\u5b66\u4e60\uff09\uff0c\u800c\u662f\u901a\u8fc7\u4e00\u4e2a\u65e0\u6bd4\u771f\u5b9e\u7684\u8fc7\u7a0b\uff1a\u4e0d\u65ad\u5730\u5c1d\u8bd5\u3001\u72af\u9519\u3001\u611f\u53d7\u7ed3\u679c\u3001\u7136\u540e\u8c03\u6574\u7b56\u7565\u3002<\/strong><\/p>\n\n\n\n \u8fd9\u79cd\u901a\u8fc7\u4e0e\u73af\u5883\u4e92\u52a8\uff0c\u6839\u636e \u201c\u5956\u52b1\u201d \u548c \u201c\u60e9\u7f5a\u201d \u6765\u5b66\u4e60\u5982\u4f55\u505a\u51fa\u6700\u4f18\u51b3\u7b56<\/strong>\u7684\u673a\u5236\uff0c\u662f\u751f\u547d\u4f53\u5b66\u4e60\u7684\u6838\u5fc3\u65b9\u5f0f\uff0c\u4e5f\u662f\u6211\u4eec\u4eca\u5929\u8981\u6df1\u5165\u63a2\u8ba8\u7684\uff0c\u673a\u5668\u5b66\u4e60\u9886\u57df\u6700\u8ff7\u4eba\u3001\u6700\u5177\u6f5c\u529b\u7684\u5206\u652f \u2014\u2014\u5f3a\u5316\u5b66\u4e60 (Reinforcement Learning, RL)<\/strong>\uff01<\/p>\n\n\n\n \u5b83\u4e0d\u662f\u6559 AI \u201c\u7b54\u6848\u662f\u4ec0\u4e48\u201d\uff0c\u800c\u662f\u4e3a AI \u8bbe\u5b9a\u4e00\u4e2a \u201c\u76ee\u6807\u201d\uff0c\u7136\u540e\u653e\u624b\u8ba9\u5b83\u5728\u865a\u62df\u4e16\u754c\u91cc\uff0c\u50cf\u4e00\u4e2a\u8e52\u8dda\u5b66\u6b65\u7684\u5b69\u5b50\u4e00\u6837\uff0c\u901a\u8fc7\u4ebf\u4e07\u6b21\u7684\u8bd5\u9519\uff0c\u81ea\u5df1\u53bb\u63a2\u7d22\u3001\u53bb\u9886\u609f\uff0c\u6700\u7ec8\u6210\u957f\u4e3a\u4e00\u4ee3\u5b97\u5e08\u3002<\/p>\n\n\n\n \u8fd9\u7bc7\u7b14\u8bb0\u5c06\u662f\u4f60\u7406\u89e3 AI \u5982\u4f55\u4ece \u201c\u88ab\u52a8\u5b66\u4e60\u201d \u8d70\u5411 \u201c\u4e3b\u52a8\u63a2\u7d22\u201d\uff0c\u4ece \u201c\u6570\u636e\u62df\u5408\u201d \u8d70\u5411 \u201c\u7b56\u7565\u4f18\u5316\u201d \u7684\u7ec8\u6781\u6307\u5357\u3002\u5b83\u975e\u5e38\u957f\uff0c\u4f46\u5c06\u4e3a\u4f60\u63ed\u5f00\u4ece AlphaGo \u5230\u81ea\u52a8\u9a7e\u9a76\uff0c\u8bb8\u591a\u524d\u6cbf AI \u6280\u672f\u80cc\u540e\u7684\u5171\u540c\u54f2\u5b66\uff01<\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \u4e00\u3001\u673a\u5668\u5b66\u4e60\u7684 \u201c\u4e09\u5927\u5bb6\u65cf\u201d\u2014 RL \u5230\u5e95\u7ad9\u5728\u4ec0\u4e48\u4f4d\u7f6e\uff1f\ud83d\udd16<\/strong><\/p>\n\n\n\n \u8981\u7406\u89e3\u5f3a\u5316\u5b66\u4e60\u7684\u72ec\u7279\u6027\uff0c\u6211\u4eec\u5fc5\u987b\u5148\u628a\u5b83\u653e\u5230\u6574\u4e2a\u673a\u5668\u5b66\u4e60\u7684\u7248\u56fe\u4e2d\uff0c\u770b\u770b\u5b83\u548c\u53e6\u5916\u4e24\u4e2a \u201c\u5927\u5bb6\u65cf\u201d\u2014\u2014 \u76d1\u7763\u5b66\u4e60\u548c\u65e0\u76d1\u7763\u5b66\u4e60 \u2014\u2014 \u5230\u5e95\u6709\u4ec0\u4e48\u6839\u672c\u4e0d\u540c\u3002<\/p>\n\n\n\n \u8fd9\u4e09\u8005\uff0c\u6784\u6210\u4e86\u673a\u5668\u5b66\u4e60\u7684\u57fa\u77f3\uff0c\u5b83\u4eec\u89e3\u51b3\u95ee\u9898\u7684\u601d\u8def\u622a\u7136\u4e0d\u540c\u3002<\/p>\n\n\n\n \ud83d\udd39 1. \u76d1\u7763\u5b66\u4e60 (Supervised Learning) – \u201c\u624b\u628a\u624b\u6559\u5b66\u7684\u4e25\u683c\u5bfc\u5e08\u201d<\/strong><\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \ud83d\udd39 2. \u65e0\u76d1\u7763\u5b66\u4e60 (Unsupervised Learning) – “\u81ea\u7531\u63a2\u7d22\u7684\u535a\u7269\u5b66\u5bb6”<\/strong><\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \ud83d\udd39 3. \u5f3a\u5316\u5b66\u4e60 (Reinforcement Learning) – \u201c\u5728\u5b9e\u8df5\u4e2d\u6210\u957f\u7684\u63a2\u9669\u5bb6\u201d<\/strong><\/p>\n\n\n\n \u603b\u7ed3\u4e00\u4e0b\u4e09\u8005\u7684\u6838\u5fc3\u533a\u522b\uff1a<\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \u4e8c\u3001\u5f3a\u5316\u5b66\u4e60\u7684 \u201c\u4e16\u754c\u89c2\u201d\u2014\u2014 \u4e94\u5927\u6838\u5fc3\u5143\u7d20 \ud83d\udd16<\/strong><\/p>\n\n\n\n \u8981\u6df1\u5165\u7406\u89e3 RL\uff0c\u6211\u4eec\u5fc5\u987b\u5148\u719f\u6089\u5b83\u7684 \u201c\u4e16\u754c\u89c2\u201d\u3002\u4efb\u4f55\u4e00\u4e2a RL \u95ee\u9898\uff0c\u90fd\u53ef\u4ee5\u88ab\u62bd\u8c61\u6210\u4e94\u4e2a\u6838\u5fc3\u5143\u7d20\uff0c\u5b83\u4eec\u5171\u540c\u6784\u6210\u4e86\u4e00\u4e2a\u540d\u4e3a\u9a6c\u5c14\u53ef\u592b\u51b3\u7b56\u8fc7\u7a0b (Markov Decision Process, MDP)<\/strong>\u7684\u6570\u5b66\u6846\u67b6\u3002<\/p>\n\n\n\n \u522b\u88ab\u8fd9\u4e2a\u540d\u5b57\u5413\u5230\uff0c\u6211\u4eec\u7528 \u201c\u6253\u602a\u5347\u7ea7\u201d \u7684\u6e38\u620f\u6765\u89e3\u91ca\uff0c\u4f60\u4f1a\u79d2\u61c2\u3002<\/p>\n\n\n\n \ud83d\udd39 1. \u667a\u80fd\u4f53 (Agent) – \u201c\u4f60\uff0c\u73a9\u5bb6\u201d<\/strong><\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \ud83d\udd39 2. \u73af\u5883 (Environment) – “\u6e38\u620f\u4e16\u754c”<\/strong><\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \ud83d\udd39 3. \u72b6\u6001 (State, S) – “\u4f60\u5f53\u524d\u7684\u60c5\u51b5”<\/strong><\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \ud83d\udd39 4. \u884c\u52a8 (Action, A) – “\u4f60\u9009\u62e9\u7684\u64cd\u4f5c”<\/strong><\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \ud83d\udd39 5. \u5956\u52b1 (Reward, R) – “\u4f60\u5f97\u5230\u7684\u5373\u65f6\u53cd\u9988”<\/strong><\/p>\n\n\n\n RL \u7684\u6574\u4e2a\u8fc7\u7a0b\uff0c\u5c31\u662f\u8fd9\u6837\u4e00\u4e2a\u65e0\u9650\u5faa\u73af\u7684 \u201c\u4e92\u52a8\u6e38\u620f\u201d\uff1a<\/p>\n\n\n\n \u667a\u80fd\u4f53\u5728 t \u65f6\u523b\u89c2\u5bdf\u5230\u73af\u5883\u7684\u72b6\u6001 S (t)\uff0c\u57fa\u4e8e\u6b64\uff0c\u5b83\u51b3\u5b9a\u91c7\u53d6\u4e00\u4e2a\u884c\u52a8 A (t)\u3002\u73af\u5883\u63a5\u6536\u5230\u8fd9\u4e2a\u884c\u52a8\u540e\uff0c\u4f1a\u8f6c\u79fb\u5230\u4e00\u4e2a\u65b0\u7684\u72b6\u6001 S (t+1)\uff0c\u5e76\u7ed9\u667a\u80fd\u4f53\u4e00\u4e2a\u5373\u65f6\u7684\u5956\u52b1 R (t+1)\u3002<\/p>\n\n\n\n \u800c\u667a\u80fd\u4f53\u7684\u7ec8\u6781\u76ee\u6807\uff0c\u4e0d\u662f\u6700\u5927\u5316\u67d0\u4e00\u4e2a\u65f6\u523b\u7684\u5373\u65f6\u5956\u52b1\uff0c\u800c\u662f\u6700\u5927\u5316\u4ece\u73b0\u5728\u5230\u672a\u6765\uff0c\u6240\u80fd\u83b7\u5f97\u7684 \u201c\u7d2f\u8ba1\u5956\u52b1\u603b\u548c\u201d\uff01\u8fd9\u88ab\u79f0\u4e3a\u56de\u62a5 (Return)<\/strong>\u3002\u4e3a\u4e86\u773c\u524d\u7684 100 \u91d1\u5e01\uff0c\u800c\u5ffd\u7565\u4e86\u65c1\u8fb9\u5373\u5c06\u79d2\u6740\u4f60\u7684\u5927 Boss\uff0c\u662f \u201c\u77ed\u89c6\u201d \u7684\uff0c\u5728 RL \u91cc\u662f\u4e0d\u53ef\u53d6\u7684\u3002<\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \u4e09\u3001AI \u5982\u4f55 \u201c\u601d\u8003\u201d\uff1f\u2014\u2014 \u7b56\u7565\u3001\u4ef7\u503c\u51fd\u6570\u4e0e Q-Learning \ud83d\udd16<\/strong><\/p>\n\n\n\n \u6211\u4eec\u77e5\u9053\u4e86 RL \u7684\u76ee\u6807\uff0c\u90a3\u4e48 AI \u7a76\u7adf\u662f\u5982\u4f55\u5b66\u4e60\uff0c\u624d\u80fd\u8fbe\u6210\u8fd9\u4e2a\u76ee\u6807\u7684\u5462\uff1f\u5b83\u7684 \u201c\u5927\u8111\u201d \u91cc\uff0c\u5230\u5e95\u5728\u60f3\u4ec0\u4e48\uff1f\u8fd9\u91cc\u9762\u4e3b\u8981\u6709\u4e09\u4e2a\u6838\u5fc3\u6982\u5ff5\u3002<\/p>\n\n\n\n \ud83d\udd39 1. \u7b56\u7565 (Policy, \u03c0) – \u201c\u6211\u7684\u884c\u52a8\u6307\u5357 \/ \u6761\u4ef6\u53cd\u5c04\u201d<\/strong><\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \ud83d\udd39 2. \u4ef7\u503c\u51fd\u6570 (Value Function, V\/Q) – \u201c\u8fd9\u4e2a\u5c40\u9762\u5bf9\u6211\u591a\u6709\u5229\uff1f\u201d<\/strong><\/p>\n\n\n\n \u60f3\u8c61\u4e00\u4e0b\uff0c\u5982\u679c\u4f60\u62e5\u6709\u4e86\u4e00\u4e2a\u5b8c\u7f8e\u7684 Q \u51fd\u6570\uff0c\u90a3\u4e48\u51b3\u7b56\u5c31\u53d8\u5f97\u5f02\u5e38\u7b80\u5355\uff1a\u5728\u5f53\u524d\u72b6\u6001 s\uff0c\u4f60\u53ea\u9700\u8981\u904d\u5386\u6240\u6709\u53ef\u4ee5\u91c7\u53d6\u7684\u884c\u52a8 a\uff0c\u8ba1\u7b97\u51fa\u6bcf\u4e00\u4e2a a \u5bf9\u5e94\u7684 Q (s, a) \u503c\uff0c\u7136\u540e\u9009\u62e9\u90a3\u4e2a\u80fd\u8ba9 Q \u503c\u6700\u5927\u7684\u884c\u52a8\uff0c\u5c31\u4e00\u5b9a\u662f\u6700\u4f18\u7684\uff01<\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \ud83d\udd39 3. Q-Learning \u7b97\u6cd5 – \u201c\u5728\u5b9e\u8df5\u4e2d\u66f4\u65b0\u6211\u7684\u5c0f\u672c\u672c\u201d<\/strong><\/p>\n\n\n\n \u90a3\u4e48\uff0c\u8fd9\u4e2a\u795e\u5947\u7684 Q \u51fd\u6570\u662f\u5982\u4f55\u5b66\u5230\u7684\u5462\uff1fQ-Learning \u5c31\u662f\u6700\u7ecf\u5178\u3001\u6700\u57fa\u7840\u7684\u5b66\u4e60\u7b97\u6cd5\u4e4b\u4e00\u3002<\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \u56db\u3001\u5f3a\u5316\u5b66\u4e60\u7684 \u201c\u6d41\u6d3e\u201d \u4e0e\u524d\u6cbf \ud83d\udd16<\/strong><\/p>\n\n\n\n RL \u7684\u4e16\u754c\u535a\u5927\u7cbe\u6df1\uff0c\u9664\u4e86\u57fa\u4e8e\u4ef7\u503c\u51fd\u6570\uff08\u5982 Q-Learning\uff09\u7684\u8fd9\u4e00\u6d3e\uff0c\u8fd8\u6709\u5176\u4ed6\u91cd\u8981\u7684\u6d41\u6d3e\uff0c\u5b83\u4eec\u4ece\u4e0d\u540c\u7684\u89d2\u5ea6\u89e3\u51b3\u95ee\u9898\u3002<\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \u4e94\u3001\u7ec8\u6781\u5c55\u671b\uff1a\u4ece \u201c\u6e38\u620f\u201d \u5230 \u201c\u73b0\u5b9e\u201d\uff0cRL \u7684\u673a\u9047\u4e0e\u6311\u6218 \ud83d\udd16<\/strong><\/p>\n\n\n\n \u5f3a\u5316\u5b66\u4e60\u63cf\u7ed8\u4e86\u4e00\u5e45\u4ee4\u4eba\u6fc0\u52a8\u7684\u672a\u6765\u56fe\u666f\uff0c\u5b83\u8ba9 AI \u6446\u8131\u4e86\u5bf9\u9759\u6001\u6570\u636e\u96c6\u7684\u4f9d\u8d56\uff0c\u8d70\u5411\u4e86\u5728\u4e0e\u771f\u5b9e\u4e16\u754c\u4ea4\u4e92\u4e2d\u81ea\u4e3b\u5b66\u4e60\u7684\u5eb7\u5e84\u5927\u9053\u3002<\/p>\n\n\n\n \u3000\u3000<\/p>\n\n\n\n \u603b\u7ed3\u65f6\u523b<\/strong> \ud83d\udd16<\/strong><\/p>\n\n\n\n \u4eca\u5929\uff0c\u6211\u4eec\u8fdb\u884c\u4e86\u4e00\u573a\u6df1\u5165 AI \u201c\u5b66\u4e60\u201d \u672c\u8d28\u7684\u4f1f\u5927\u63a2\u7d22\u3002\u73b0\u5728\uff0c\u8ba9\u6211\u4eec\u6765\u603b\u7ed3\u4e00\u4e0b\u5173\u4e8e \u201c\u5f3a\u5316\u5b66\u4e60\u201d \u7684\u6838\u5fc3\u5b9d\u85cf\uff1a<\/p>\n\n\n\n \u638c\u63e1\u4e86 \u201c\u5f3a\u5316\u5b66\u4e60\u201d\uff0c\u4f60\u5c31\u7406\u89e3\u4e86 AI \u5982\u4f55\u4ece\u4e00\u4e2a\u53ea\u4f1a \u201c\u80cc\u4e66\u201d \u7684\u5b66\u751f\uff0c\u8fdb\u5316\u4e3a\u4e00\u4e2a\u80fd\u591f\u5728\u590d\u6742\u4e16\u754c\u4e2d\uff0c\u901a\u8fc7\u5b9e\u8df5\u548c\u53cd\u601d\uff0c\u81ea\u4e3b\u5bfb\u627e\u6210\u529f\u4e4b\u8def\u7684 \u201c\u63a2\u9669\u5bb6\u201d\u3002\u8fd9\uff0c\u6216\u8bb8\u624d\u662f\u901a\u5f80\u771f\u6b63\u901a\u7528\u4eba\u5de5\u667a\u80fd\uff08AGI\uff09\u7684\u3001\u66f4\u5177\u542f\u53d1\u6027\u7684\u9053\u8def\u3002<\/p>\n","protected":false},"excerpt":{"rendered":" \u8f6c\u8f7d\uff1a\u5c0f\u7ea2\u4e66 AI\u4ea7\u54c1\u8d75\u54e5 \u524d\u8a00 \ud83d\udd16 \u5728\u5f00\u59cb\u4eca\u5929\u7684\u8bdd\u9898\u524d\uff0c\u8bf7\u4f60\u56de\u60f3\u4e00\u4e0b\uff0c\u4f60\u662f\u5982\u4f55\u5b66\u4f1a\u4e00\u9879\u65b0\u6280\u80fd\u7684\uff1f\u6bd4\u5982\u9a91\u81ea\u884c\u8f66 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48],"tags":[],"_links":{"self":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/21619"}],"collection":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=21619"}],"version-history":[{"count":1,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/21619\/revisions"}],"predecessor-version":[{"id":21621,"href":"https:\/\/92it.top\/index.php?rest_route=\/wp\/v2\/posts\/21619\/revisions\/21621"}],"wp:attachment":[{"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=21619"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=21619"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/92it.top\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=21619"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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<\/th> \u76d1\u7763\u5b66\u4e60<\/th> \u65e0\u76d1\u7763\u5b66\u4e60<\/th> \u5f3a\u5316\u5b66\u4e60<\/th><\/tr><\/thead> \u5b66\u4e60\u4fe1\u53f7<\/td> \u660e\u786e\u7684\u6807\u7b7e \/ \u6807\u51c6\u7b54\u6848<\/td> \u6570\u636e\u81ea\u8eab\u7684\u5185\u5728\u7ed3\u6784<\/td> \u73af\u5883\u53cd\u9988\u7684\u5956\u52b1 \/ \u60e9\u7f5a\u4fe1\u53f7<\/td><\/tr> \u6570\u636e\u5f62\u5f0f<\/td> \u9759\u6001\u7684\u3001\u5df2\u6807\u6ce8\u7684\u6570\u636e\u96c6<\/td> \u9759\u6001\u7684\u3001\u672a\u6807\u6ce8\u7684\u6570\u636e\u96c6<\/td> \u52a8\u6001\u7684\u3001\u901a\u8fc7\u4e0e\u73af\u5883\u4ea4\u4e92\u4ea7\u751f\u7684\u6570\u636e\u6d41<\/td><\/tr> \u6838\u5fc3\u76ee\u6807<\/td> \u5b66\u4e60\u4e00\u4e2a\u4ece\u8f93\u5165\u5230\u8f93\u51fa\u7684\u6620\u5c04\u51fd\u6570\uff08\u5206\u7c7b \/ \u56de\u5f52\uff09<\/td> \u53d1\u73b0\u6570\u636e\u4e2d\u7684\u9690\u85cf\u6a21\u5f0f\uff08\u805a\u7c7b \/ \u964d\u7ef4\uff09<\/td> \u5b66\u4e60\u4e00\u4e2a\u6700\u4f18\u7684\u884c\u52a8\u7b56\u7565\uff0c\u4ee5\u6700\u5927\u5316\u957f\u671f\u7d2f\u8ba1\u5956\u52b1<\/td><\/tr> \u95ee\u9898\u7c7b\u578b<\/td> “\u8fd9\u662f\u4ec0\u4e48\uff1f”<\/td> “\u5b83\u4eec\u6709\u4f55\u4e0d\u540c \/ \u76f8\u540c\uff1f”<\/td> “\u63a5\u4e0b\u6765\u6211\u8be5\u600e\u4e48\u505a\uff1f”<\/td><\/tr> AI \u89d2\u8272<\/td> \u4e00\u4e2a\u6a21\u4eff\u8005 (Imitator)<\/td> \u4e00\u4e2a\u5206\u6790\u5e08 (Analyst)<\/td> \u4e00\u4e2a\u51b3\u7b56\u8005 (Decision Maker)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
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