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<time title="创建时间：2022-09-05 23:04:45 / 修改时间：23:06:16" itemprop="dateCreated datePublished" datetime="2022-09-05T23:04:45+08:00">2022-09-05</time>
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<p>在做离散化时，空间维度越高，需要探索的组合就越多，这会造成维度灾难。连续控制可以在一定程度上解决这个问题<br>
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<a href="/2022/09/05/rl4/" class="post-title-link" itemprop="url">强化学习系列：策略梯度优化</a>
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<h2 id="策略梯度"><a href="#策略梯度" class="headerlink" title="策略梯度"></a>策略梯度</h2><ul>
<li>策略网络： <mjx-container class="MathJax" jax="SVG"><svg style="vertical-align: -0.566ex" xmlns="http://www.w3.org/2000/svg" width="8.004ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 3537.7 1000"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><path data-c="1D70B" d="M132 -11Q98 -11 98 22V33L111 61Q186 219 220 334L228 358H196Q158 358 142 355T103 336Q92 329 81 318T62 297T53 285Q51 284 38 284Q19 284 19 294Q19 300 38 329T93 391T164 429Q171 431 389 431Q549 431 553 430Q573 423 573 402Q573 371 541 360Q535 358 472 358H408L405 341Q393 269 393 222Q393 170 402 129T421 65T431 37Q431 20 417 5T381 -10Q370 -10 363 -7T347 17T331 77Q330 86 330 121Q330 170 339 226T357 318T367 358H269L268 354Q268 351 249 275T206 114T175 17Q164 -11 132 -11Z"></path></g><g data-mml-node="mo" transform="translate(570,0)"><path data-c="28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path></g><g data-mml-node="mi" transform="translate(959,0)"><path data-c="1D44E" d="M33 157Q33 258 109 349T280 441Q331 441 370 392Q386 422 416 422Q429 422 439 414T449 394Q449 381 412 234T374 68Q374 43 381 35T402 26Q411 27 422 35Q443 55 463 131Q469 151 473 152Q475 153 483 153H487Q506 153 506 144Q506 138 501 117T481 63T449 13Q436 0 417 -8Q409 -10 393 -10Q359 -10 336 5T306 36L300 51Q299 52 296 50Q294 48 292 46Q233 -10 172 -10Q117 -10 75 30T33 157ZM351 328Q351 334 346 350T323 385T277 405Q242 405 210 374T160 293Q131 214 119 129Q119 126 119 118T118 106Q118 61 136 44T179 26Q217 26 254 59T298 110Q300 114 325 217T351 328Z"></path></g><g data-mml-node="mo" transform="translate(1488,0) translate(0 -0.5)"><path data-c="7C" d="M139 -249H137Q125 -249 119 -235V251L120 737Q130 750 139 750Q152 750 159 735V-235Q151 -249 141 -249H139Z"></path></g><g data-mml-node="mi" transform="translate(1766,0)"><path data-c="1D460" d="M131 289Q131 321 147 354T203 415T300 442Q362 442 390 415T419 355Q419 323 402 308T364 292Q351 292 340 300T328 326Q328 342 337 354T354 372T367 378Q368 378 368 379Q368 382 361 388T336 399T297 405Q249 405 227 379T204 326Q204 301 223 291T278 274T330 259Q396 230 396 163Q396 135 385 107T352 51T289 7T195 -10Q118 -10 86 19T53 87Q53 126 74 143T118 160Q133 160 146 151T160 120Q160 94 142 76T111 58Q109 57 108 57T107 55Q108 52 115 47T146 34T201 27Q237 27 263 38T301 66T318 97T323 122Q323 150 302 164T254 181T195 196T148 231Q131 256 131 289Z"></path></g><g data-mml-node="mo" transform="translate(2235,0)"><path data-c="3B" d="M78 370Q78 394 95 412T138 430Q162 430 180 414T199 371Q199 346 182 328T139 310T96 327T78 370ZM78 60Q78 85 94 103T137 121Q202 121 202 8Q202 -44 183 -94T144 -169T118 -194Q115 -194 106 -186T95 -174Q94 -171 107 -155T137 -107T160 -38Q161 -32 162 -22T165 -4T165 4Q165 5 161 4T142 0Q110 0 94 18T78 60Z"></path></g><g data-mml-node="mi" transform="translate(2679.7,0)"><path data-c="1D703" d="M35 200Q35 302 74 415T180 610T319 704Q320 704 327 704T339 705Q393 701 423 656Q462 596 462 495Q462 380 417 261T302 66T168 -10H161Q125 -10 99 10T60 63T41 130T35 200ZM383 566Q383 668 330 668Q294 668 260 623T204 521T170 421T157 371Q206 370 254 370L351 371Q352 372 359 404T375 484T383 566ZM113 132Q113 26 166 26Q181 26 198 36T239 74T287 161T335 307L340 324H145Q145 321 136 286T120 208T113 132Z"></path></g><g data-mml-node="mo" transform="translate(3148.7,0)"><path data-c="29" d="M60 749L64 750Q69 750 74 750H86L114 726Q208 641 251 514T294 250Q294 182 284 119T261 12T224 -76T186 -143T145 -194T113 -227T90 -246Q87 -249 86 -250H74Q66 -250 63 -250T58 -247T55 -238Q56 -237 66 -225Q221 -64 221 250T66 725Q56 737 55 738Q55 746 60 749Z"></path></g></g></g></svg></mjx-container>，用于控制agent </li>
<li><p>状态价值函数： </p>
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<p>多智能体四种常见设定：<br>
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<h2 id="sarsastate-action-reward-state-action算法">Sarsa(State-Action-Reward-State-Action)算法</h2>
<p>折扣回报的定义：
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<p>日常开发中多个场景都会用到多线程开发，如数据并行读取、并行调用模型获取预估值等。下面对多线程的用法做一个简单总结</p>
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<h2 id="1-专业术语"><a href="#1-专业术语" class="headerlink" title="1.专业术语"></a>1.专业术语</h2><ul>
<li>state：状态</li>
<li>action：动作</li>
<li>policy：<mjx-container class="MathJax" jax="SVG"><svg style="vertical-align: -0.566ex" xmlns="http://www.w3.org/2000/svg" width="5.937ex" height="2.262ex" role="img" focusable="false" viewBox="0 -750 2624 1000"><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mi"><path data-c="1D70B" d="M132 -11Q98 -11 98 22V33L111 61Q186 219 220 334L228 358H196Q158 358 142 355T103 336Q92 329 81 318T62 297T53 285Q51 284 38 284Q19 284 19 294Q19 300 38 329T93 391T164 429Q171 431 389 431Q549 431 553 430Q573 423 573 402Q573 371 541 360Q535 358 472 358H408L405 341Q393 269 393 222Q393 170 402 129T421 65T431 37Q431 20 417 5T381 -10Q370 -10 363 -7T347 17T331 77Q330 86 330 121Q330 170 339 226T357 318T367 358H269L268 354Q268 351 249 275T206 114T175 17Q164 -11 132 -11Z"></path></g><g data-mml-node="mo" transform="translate(570,0)"><path data-c="28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path></g><g data-mml-node="mi" transform="translate(959,0)"><path data-c="1D44E" d="M33 157Q33 258 109 349T280 441Q331 441 370 392Q386 422 416 422Q429 422 439 414T449 394Q449 381 412 234T374 68Q374 43 381 35T402 26Q411 27 422 35Q443 55 463 131Q469 151 473 152Q475 153 483 153H487Q506 153 506 144Q506 138 501 117T481 63T449 13Q436 0 417 -8Q409 -10 393 -10Q359 -10 336 5T306 36L300 51Q299 52 296 50Q294 48 292 46Q233 -10 172 -10Q117 -10 75 30T33 157ZM351 328Q351 334 346 350T323 385T277 405Q242 405 210 374T160 293Q131 214 119 129Q119 126 119 118T118 106Q118 61 136 44T179 26Q217 26 254 59T298 110Q300 114 325 217T351 328Z"></path></g><g data-mml-node="mo" transform="translate(1488,0) translate(0 -0.5)"><path data-c="7C" d="M139 -249H137Q125 -249 119 -235V251L120 737Q130 750 139 750Q152 750 159 735V-235Q151 -249 141 -249H139Z"></path></g><g data-mml-node="mi" transform="translate(1766,0)"><path data-c="1D460" d="M131 289Q131 321 147 354T203 415T300 442Q362 442 390 415T419 355Q419 323 402 308T364 292Q351 292 340 300T328 326Q328 342 337 354T354 372T367 378Q368 378 368 379Q368 382 361 388T336 399T297 405Q249 405 227 379T204 326Q204 301 223 291T278 274T330 259Q396 230 396 163Q396 135 385 107T352 51T289 7T195 -10Q118 -10 86 19T53 87Q53 126 74 143T118 160Q133 160 146 151T160 120Q160 94 142 76T111 58Q109 57 108 57T107 55Q108 52 115 47T146 34T201 27Q237 27 263 38T301 66T318 97T323 122Q323 150 302 164T254 181T195 196T148 231Q131 256 131 289Z"></path></g><g data-mml-node="mo" transform="translate(2235,0)"><path data-c="29" d="M60 749L64 750Q69 750 74 750H86L114 726Q208 641 251 514T294 250Q294 182 284 119T261 12T224 -76T186 -143T145 -194T113 -227T90 -246Q87 -249 86 -250H74Q66 -250 63 -250T58 -247T55 -238Q56 -237 66 -225Q221 -64 221 250T66 725Q56 737 55 738Q55 746 60 749Z"></path></g></g></g></svg></mjx-container>，指给定状态s下采取动作a的概率
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<h2 id="时间复杂度">时间复杂度</h2>
<p>时间复杂度是一个函数，它定性描述算法的运行时间<br />
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<p>本文的目的是梳理工作中实用且常用预估模型，一方面，可以在工作中有需要时，快速拿来复用验证一些想法；另一方面，有助于进一步理解这些基础算法模型的原理和实现。梳理的模型主要有以下几种：
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<a href="/2022/03/06/rerankalg/" class="post-title-link" itemprop="url">基于上下文感知的重排序算法梳理</a>
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<h1 id="globally-optimized-mutual-influence-aware-ranking-in-e-commerce-searchmirnn-2018">Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search(miRNN, 2018)</h1>
<p>主要贡献：</p>
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<li>提出使用beam search方法来生成最优序列的思路</li>
<li>提出使用RNN和注意力机制来建模不同item之间的相互影响</li>
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