Papers by Masaki Ono
LOA: Logical Optimal Actions for Text-based Interaction Games (2021.acl-demo)
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Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray
| Challenge: | et al., 2019) have proposed a neuro-symbolic approach for reinforcement learning in non-simultaneous environments. |
| Approach: | They propose an action decision architecture with a neuro-symbolic framework for natural language interaction games. |
| Outcome: | The proposed framework provides an open-source implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. |
Neuro-Symbolic Reinforcement Learning with First-Order Logic (2021.emnlp-main)
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Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, Alexander Gray
| Challenge: | Existing deep reinforcement learning methods require many trials before convergence and no direct interpretability of trained policies is provided. |
| Approach: | They propose a novel RL method which can learn symbolic and interpretable rules in their differentiable network. |
| Outcome: | The proposed method can learn symbolic and interpretable rules in their differentiable network. |
Neuro-Symbolic Approaches for Text-Based Policy Learning (2021.emnlp-main)
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| Challenge: | Text-based games are important testbeds for reinforcement learning in the natural language domain. |
| Approach: | They propose a method that learns interpretable action policy rules from symbolic abstractions of textual observations for improved generalization. |
| Outcome: | The proposed method outperforms existing methods in RL using 5-10x fewer training games. |