This variant arises in several strategic settings, such as learning how to bid in non-truthful repeated auctions, which has gained a lot of attention lately as many platforms have switched to running first-price auctions. We call this problem the contextual bandits problem with cross-learning. ...
Cold start —The NBA platform can be cold-started, learning and adapting on its own within a few days based on newly collected reaction data. When faced with a new use case involving brand-new actions that have never been exposed to users, traditional ML models typically require separate proc...
Hierarchical Exploration for Accelerating Contextual Bandits Contextual bandit learning is an increasingly popular approach to optimizing recommender systems via user feedback, but can be slow to converge in practice... Y Yue,SA Hong,C Guestrin - Omnipress 被引量: 28发表: 2012年 Measuring Context-Sp...
RL’s key challenge is the trade-off between holding the current choice and learning novel ones, officially recognized as the exploitation-exploration dilemma. Multi-armed bandits (MABs), firstly suggested by Auer [10], can efficiently deal with such trade-off. In MAB, a player interacts with...
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.) - facebookresearch/ReAgent