2 citations · 2 across the 5 of their papers we have counts for
5 papers
WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptation
Zahin Sufiyan, Shadan Golestan, Yoshihiro Mitsuka +2
Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retr…
ViSA: Visited-State Augmentation for Generalized Goal-Space Contrastive Reinforcement Learning
Issa Nakamura, Tomoya Yamanokuchi, Yuki Kadokawa +5
Goal-Conditioned Reinforcement Learning (GCRL) is a framework for learning a policy that can reach arbitrarily given goals. In particular, Contrastive Reinforcement Learning (CRL)…
A Study of the Efficacy of Generative Flow Networks for Robotics and Machine Fault-Adaptation
Zahin Sufiyan, Shadan Golestan, Shotaro Miwa +2
Advancements in robotics have opened possibilities to automate tasks in various fields such as manufacturing, emergency response and healthcare. However, a significant challenge th…
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions
Yoshihiro Mitsuka, Shadan Golestan, Zahin Sufiyan +2
Meta-learning enables models to rapidly adapt to new tasks by leveraging prior experience, but its adaptation mechanisms remain opaque, especially regarding how past training tasks…
Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms
Sheila Schoepp, Mehran Taghian, Shotaro Miwa +3
Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults. Conventional fault-tolerant design duplica…