no code implementations • 27 Mar 2024 • Taku Yamagata, Raul Santos-Rodriguez
Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges related to safety and robustness.
no code implementations • 6 Feb 2023 • Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez, Ian Craddock, Miquel Perello Nieto, Raul Santos-Rodriguez, Weisong Yang, Peter Flach
Here we propose a method to model human biases on temporal annotations and argue for the use of soft labels.
no code implementations • 8 Sep 2022 • Taku Yamagata, Ahmed Khalil, Raul Santos-Rodriguez
The Decision Transformer (DT) combines the conditional policy approach and a transformer architecture, showing competitive performance against several benchmarks.
no code implementations • 16 Nov 2021 • Taku Yamagata, Ryan McConville, Raul Santos-Rodriguez
We empirically show that our approach can accurately learn the reliability of each trainer correctly and use it to maximise the information gained from the multiple trainers' feedback, even if some of the sources are adversarial.
no code implementations • 13 Oct 2020 • Taku Yamagata, Aisling O'Kane, Amid Ayobi, Dmitri Katz, Katarzyna Stawarz, Paul Marshall, Peter Flach, Raúl Santos-Rodríguez
In this paper we investigate the use of model-based reinforcement learning to assist people with Type 1 Diabetes with insulin dose decisions.
no code implementations • 16 Aug 2019 • Taku Yamagata, Raúl Santos-Rodríguez, Ryan McConville, Atis Elsts
However, few studies have considered the balance between wearable power consumption and activity recognition accuracy.