33 citations · 76 across the 10 of their papers we have counts for
4 papers · 1 filter
DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback
Riku Arakawa, Sosuke Kobayashi, Yuya Unno +2
Exploration has been one of the greatest challenges in reinforcement learning (RL), which is a large obstacle in the application of RL to robotics. Even with state-of-the-art RL al…
BayesGrad: Explaining Predictions of Graph Convolutional Networks
Hirotaka Akita, Kosuke Nakago, Tomoki Komatsu +4
Recent advances in graph convolutional networks have significantly improved the performance of chemical predictions, raising a new research question: "how do we explain the predict…
Neural Multi-scale Image Compression
Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato +1
This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder…
Clipped Action Policy Gradient
Yasuhiro Fujita, Shin-ichi Maeda
Many continuous control tasks have bounded action spaces. When policy gradient methods are applied to such tasks, out-of-bound actions need to be clipped before execution, while po…