all AI news
$\epsilon$-Policy Gradient for Online Pricing
May 7, 2024, 4:43 a.m. | Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang
cs.LG updates on arXiv.org arxiv.org
Abstract: Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an $\epsilon$-policy gradient algorithm for the online pricing learning task. The algorithm extends $\epsilon$-greedy algorithm by replacing greedy exploitation with gradient descent step and facilitates learning via model inference. We optimize the regret of the proposed algorithm by quantifying the exploration cost in terms of the exploration probability $\epsilon$ and the exploitation cost in terms of the gradient descent optimization and gradient estimation …
abstract algorithm arxiv cs.lg epsilon exploitation free gradient inference math.oc paper policy pricing q-fin.st reinforcement reinforcement learning stat.ml the algorithm type via
More from arxiv.org / cs.LG updates on arXiv.org
Efficient Data-Driven MPC for Demand Response of Commercial Buildings
2 days, 17 hours ago |
arxiv.org
Testing the Segment Anything Model on radiology data
2 days, 17 hours ago |
arxiv.org
Calorimeter shower superresolution
2 days, 17 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US