3 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
What model does MuZero learn?
Jinke He, Thomas M. Moerland, Joery A. de Vries +1
Model-based reinforcement learning (MBRL) has drawn considerable interest in recent years, given its promise to improve sample efficiency. Moreover, when using deep-learned models,…
Online Planning in POMDPs with Self-Improving Simulators
Jinke He, Miguel Suau, Hendrik Baier +2
How can we plan efficiently in a large and complex environment when the time budget is limited? Given the original simulator of the environment, which may be computationally very d…
Influence-Augmented Online Planning for Complex Environments
Jinke He, Miguel Suau, Frans A. Oliehoek
How can we plan efficiently in real time to control an agent in a complex environment that may involve many other agents? While existing sample-based planners have enjoyed empirica…