Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p

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Multi-Objective Actor-Critics for?Real-Time Bidding in?Display Advertisingering display cost, Return on Investment (ROI), and other influential Key Performance Indicators (KPIs), large ad platforms try to balance the trade-off among various goals in dynamics. To address the challenge, we propose a .ulti-.bjec.ve .ctor-.ritics algorithm based on reinforcement learning (RL)

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Oracle-SAGE: Planning Ahead in?Graph-Based Deep Reinforcement Learninginput. Where available (such as some robotic control domains), low dimensional vector inputs outperform their image based counterparts, but it is challenging to represent complex dynamic environments in this manner. Relational reinforcement learning instead represents the world as a set of objects a

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State Representation Learning for?Goal-Conditioned Reinforcement Learningdding space where distances between pairs of embedded states correspond to the minimum number of actions needed to transition between them. Compared to previous methods, our approach does not require any domain knowledge, learning from offline and unlabeled data. We show how this representation can

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Imitation Learning with?Sinkhorn Distances experts and learners is crucial in their effectiveness in learning from demonstrations. In this paper, we present tractable solutions by formulating imitation learning as minimization of the Sinkhorn distance between occupancy measures. The formulation combines the valuable properties of optimal tr

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Safe Exploration Method for?Reinforcement Learning Under Existence of?Disturbance property, we have to take the risk into consideration when we apply those algorithms to safety-critical problems especially in real environments. In this study, we deal with a safe exploration problem in reinforcement learning under the existence of disturbance. We define the safety during learning

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