3 citations · 8 across the 4 of their papers we have counts for
4 papers
Enhancing Reusability of Learned Skills for Robot Manipulation via Gaze Information and Motion Bottlenecks
Ryo Takizawa, Izumi Karino, Koki Nakagawa +2
Autonomous agents capable of diverse object manipulations should be able to acquire a wide range of manipulation skills with high reusability. Although advances in deep learning ha…
Reinforced Imitation Learning by Free Energy Principle
Ryoya Ogishima, Izumi Karino, Yasuo Kuniyoshi
Reinforcement Learning (RL) requires a large amount of exploration especially in sparse-reward settings. Imitation Learning (IL) can learn from expert demonstrations without explor…
Identifying Critical States by the Action-Based Variance of Expected Return
Izumi Karino, Yoshiyuki Ohmura, Yasuo Kuniyoshi
The balance of exploration and exploitation plays a crucial role in accelerating reinforcement learning (RL). To deploy an RL agent in human society, its explainability is also ess…
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning
Izumi Karino, Kazutoshi Tanaka, Ryuma Niiyama +1
This paper proposes an exploration method for deep reinforcement learning based on parameter space noise. Recent studies have experimentally shown that parameter space noise result…