Don Joven Agravante
Neuro-Symbolic Reinforcement Learning with First-Order Logic
Daiki Kimura | Masaki Ono | Subhajit Chaudhury | Ryosuke Kohita | Akifumi Wachi | Don Joven Agravante | Michiaki Tatsubori | Asim Munawar | Alexander Gray
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
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-symbolic framework called Logical Neural Network, which can learn symbolic and interpretable rules in their differentiable network. The method is first to extract first-order logical facts from text observation and external word meaning network (ConceptNet), then train a policy in the network with directly interpretable logical operators. Our experimental results show RL training with the proposed method converges significantly faster than other state-of-the-art neuro-symbolic methods in a TextWorld benchmark.
LOA: Logical Optimal Actions for Text-based Interaction Games
Daiki Kimura | Subhajit Chaudhury | Masaki Ono | Michiaki Tatsubori | Don Joven Agravante | Asim Munawar | Akifumi Wachi | Ryosuke Kohita | Alexander Gray
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games. The demonstration for LOA experiments consists of a web-based interactive platform for text-based games and visualization for acquired knowledge for improving interpretability for trained rules. This demonstration also provides a comparison module with other neuro-symbolic approaches as well as non-symbolic state-of-the-art agent models on the same text-based games. Our LOA also provides open-sourced implementation in Python for the reinforcement learning environment to facilitate an experiment for studying neuro-symbolic agents. Demo site: https://ibm.biz/acl21-loa, Code: https://github.com/ibm/loa
- Daiki Kimura 2
- Masaki Ono 2
- Subhajit Chaudhury 2
- Ryosuke Kohita 2
- Akifumi Wachi 2
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