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Comparison Lift: Bandit-based Experimentation System for Online Advertising
Second, by adapting experimental design to information acquired during the test, it reduces substantially the cost of experimentation to the advertiser.
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning
The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances.
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
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.
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