125 citations · 366 across the 33 of their papers we have counts for
7 papers · 1 filter
Generalized Decision Transformer for Offline Hindsight Information Matching
Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu
How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for p…
Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima +2
Recently many algorithms were devised for reinforcement learning (RL) with function approximation. While they have clear algorithmic distinctions, they also have many implementatio…
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno +4
Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In…
Group Equivariant Conditional Neural Processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai +2
We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural proce…
Wheelchair Behavior Recognition for Visualizing Sidewalk Accessibility by Deep Neural Networks
Takumi Watanabe, Hiroki Takahashi, Goh Sato +3
This paper introduces our methodology to estimate sidewalk accessibilities from wheelchair behavior via a triaxial accelerometer in a smartphone installed under a wheelchair seat.…
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo +2
Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that…
Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization
Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Learning domain-invariant representation is a dominant approach for domain generalization (DG), where we need to build a classifier that is robust toward domain shifts. However, pr…