77 citations · 189 across the 16 of their papers we have counts for
6 papers · 1 filter
Variational Recurrent Models for Solving Partially Observable Control Tasks
Dongqi Han, Kenji Doya, Jun Tani
In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how…
Optimization and Validation of Diffusion MRI-based Fiber Tracking with Neural Tracer Data as a Reference
Carlos Enrique Gutierrez, Henrik Skibbe, Ken Nakae +11
Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can potentially help to reveal the brain's global network…
MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography
Henrik Skibbe, Akiya Watakabe, Ken Nakae +10
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information. In the Brain/MINDS project, a connectivity study on…
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning
Tadashi Kozuno, Dongqi Han, Kenji Doya
In real-world applications of reinforcement learning (RL), noise from inherent stochasticity of environments is inevitable. However, current policy evaluation algorithms, which pla…
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Paavo Parmas, Carl Edward Rasmussen, Jan Peters +1
Previously, the exploding gradient problem has been explained to be central in deep learning and model-based reinforcement learning, because it causes numerical issues and instabil…
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
Dongqi Han, Kenji Doya, Jun Tani
Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown distinct advantages, e.g., solving memory-dependent tasks and meta-learning. However, little effort has…