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LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions

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arxiv 1708.05565 v2 pith:PMPQ6P7B submitted 2017-08-18 cs.LG cs.AIcs.CLcs.GT

LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions

Yu Wang , Jiayi Liu , Yuxiang Liu , Jun Hao , Yang He , Jinghe Hu , Weipeng P. Yan , Mantian Li This is my paper
classification cs.LG cs.AIcs.CLcs.GT
keywords agentbiddingonlinereal-timeauctionsdeepinformationinputs
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information. The agent is based on an asynchronous stochastic variant of DQN (Deep Q Network) named DASQN. The inputs of the agent are plain-text descriptions of states of a game of incomplete information, i.e. real-time large scale online auctions, and the rewards are auction profits of very large scale. We apply the agent to an essential portion of JD's online RTB (real-time bidding) advertising business and find that it easily beats the former state-of-the-art bidding policy that had been carefully engineered and calibrated by human experts: during JD.com's June 18th anniversary sale, the agent increased the company's ads revenue from the portion by more than 50%, while the advertisers' ROI (return on investment) also improved significantly.

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    Diffusion-completer training with a trajectory aligner makes diffusion-based auto-bidding work at scale, improving conversion value by 29.9% on a sparse public benchmark and by 2.0% in production at Kuaishou.