15 citations · 40 across the 25 of their papers we have counts for
5 papers · 1 filter
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching
Hana Hoshino, Kei Ota, Asako Kanezaki +1
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious. However, prior IRL algorithms use on-policy transitions, which require inte…
Training Larger Networks for Deep Reinforcement Learning
Kei Ota, Devesh K. Jha, Asako Kanezaki
The success of deep learning in the computer vision and natural language processing communities can be attributed to training of very deep neural networks with millions or billions…
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
Kei Ota, Devesh K. Jha, Diego Romeres +7
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success…
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
Kei Ota, Yoko Sasaki, Devesh K. Jha +2
In this paper, we consider the problem of building learning agents that can efficiently learn to navigate in constrained environments. The main goal is to design agents that can ef…
Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?
Kei Ota, Tomoaki Oiki, Devesh K. Jha +2
Deep reinforcement learning (RL) algorithms have recently achieved remarkable successes in various sequential decision making tasks, leveraging advances in methods for training lar…