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
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
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning. Appl. Energy 2021, 281, 116117. [Google Scholar] [CrossRef] Bouneffouf, D.; Rish, I.; Cecchi, G.A.; Feraud, R. Context attentive bandits: Contextual bandit with restricted context. arXiv 2017, ...
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning. Appl. Energy 2021, 281, 116117. [Google Scholar] [CrossRef] Bouneffouf, D.; Rish, I.; Cecchi, G.A.; Feraud, R. Context attentive bandits: Contextual bandit with restricted context. arXiv 2017, ...
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning. Appl. Energy 2021, 281, 116117. [Google Scholar] [CrossRef] Bouneffouf, D.; Rish, I.; Cecchi, G.A.; Feraud, R. Context attentive bandits: Contextual bandit with restricted context. arXiv 2017, ...
Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning. Appl. Energy 2021, 281, 116117. [CrossRef] 15. Bouneffouf, D.; Rish, I.; Cecchi, G.A.; Feraud, R. Context attentive bandits: Contextual bandit with restricted context. arXiv 2017, arXiv:...