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

Understand

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.