Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep Reinforcement Learning

<div>Maximum <svg height="10.7866pt" id="M168" style="vertical-align:-2.150701pt" version="1.1" viewbox="-0.0498162 -8.6359 9.52083 10.7866" width="9.52083pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M699 368C699 549 574 666 407 666C186 666 23 488 23 277C23 113 129 -3 288 -13L307 -26C431 -111 501 -139 533 -147C559 -154 613 -163 658 -164L666 -141C597 -111 507 -66 430 -11L416 -1C580 42 699 190 699 368ZM601 371C601 227 518 54 381 22L354 40L278 24C175 47 120 145 120 269C120 451 235 631 398 631C540 631 601 521 601 371Z"></path></g></svg> value maximum flow model.</div>

Wireless Communications and Mobile Computing

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Figure 4

Figure 4: Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep Reinforcement Learning 