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

18 Aug 2017 Yu Wang Jiayi Liu Yuxiang Liu Jun Hao Yang He Jinghe Hu Weipeng P. Yan Mantian Li

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... (read more)

PDF Abstract
No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper