Masaki Ono — accepted AI-conference papers

2021

Neuro-Symbolic Approaches for Text-Based Policy Learning

Subhajit Chaudhury, Prithviraj Sen, Masaki Ono, Daiki Kimura, Michiaki Tatsubori, Asim Munawar

EMNLP 2021main

Text-Based Games (TBGs) have emerged as important testbeds for reinforcement learning (RL) in the natural language domain. Previous methods using LSTM-based action policies are uninterpretable and often overfit the training games showing poor performance to unseen test games. We present SymboLic Act…

Cited by 22 Source ↗PDF ↗code ↗Scholar ↗

2021

Neuro-Symbolic Reinforcement Learning with First-Order Logic

Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante +3

EMNLP 2021main

Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided. In order to achieve fast convergence and interpretability for the policy in RL, we propose a novel RL method for text-based games with a recent neuro…

Cited by 48 Source ↗PDF ↗Scholar ↗