3 papers
cs.LG2026
VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learning
Joery A. de Vries, Jinke He, Yaniv Oren +3
Optimally trading-off exploration and exploitation is the holy grail of reinforcement learning as it promises maximal data-efficiency for solving any task. Bayes-optimal agents ach…
cs.LG2025
Learning to Focus: Prioritizing Informative Histories with Structured Attention Mechanisms in Partially Observable Reinforcement Learning
Daniel De Dios Allegue, Jinke He, Frans A. Oliehoek
Transformers have shown strong ability to model long-term dependencies and are increasingly adopted as world models in model-based reinforcement learning (RL) under partial observa…
cs.LG2025
Timing the Match: A Deep Reinforcement Learning Approach for Ride-Hailing and Ride-Pooling Services
Yiman Bao, Jie Gao, Jinke He +2
Efficient timing in ride-matching is crucial for improving the performance of ride-hailing and ride-pooling services, as it determines the number of drivers and passengers consider…