# Extracting Action Sequences from Texts Based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1803.02632 · HTML: https://mlsanity.com/paper/1803.02632/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2018.

## Built on by (most cited first, 12 of 12)

- [Translating Natural Language to Planning Goals with Large-Language Models](https://mlsanity.com/paper/2302.05128/) (2023)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [GPT3-to-plan: Extracting plans from text using GPT-3](https://mlsanity.com/paper/2106.07131/) (2021)
- [Classical Planning in Deep Latent Space](https://mlsanity.com/paper/2107.00110/) (2021)
- [Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill Diversity](https://mlsanity.com/paper/2202.02886/) (2022)
- [Planning with Logical Graph-based Language Model for Instruction Generation](https://mlsanity.com/paper/2308.13782/) (2023)
- [Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge](https://mlsanity.com/paper/2202.07138/) (2022)
- [Automated Action Model Acquisition from Narrative Texts](https://mlsanity.com/paper/2307.10247/) (2023)
- [Text-Based Action-Model Acquisition for Planning](https://mlsanity.com/paper/2202.08373/) (2022)
- [Learning to Play Chess from Textbooks (LEAP): a Corpus for Evaluating Chess Moves based on Sentiment Analysis](https://mlsanity.com/paper/2310.20260/) (2023)
- [Towards Explainable Social Agent Authoring tools: A case study on FAtiMA-Toolkit](https://mlsanity.com/paper/2206.03360/) (2022)
- [CHARET: Character-centered Approach to Emotion Tracking in Stories](https://mlsanity.com/paper/2102.07537/) (2021)

## Abstract (arXiv)

Extracting action sequences from natural language texts is challenging, as it requires commonsense inferences based on world knowledge. Although there has been work on extracting action scripts, instructions, navigation actions, etc., they require that either the set of candidate actions be provided in advance, or that action descriptions are restricted to a specific form, e.g., description templates. In this paper, we aim to extract action sequences from texts in free natural language, i.e., without any restricted templates, provided the candidate set of actions is unknown. We propose to extract action sequences from texts based on the deep reinforcement learning framework. Specifically, we view "selecting" or "eliminating" words from texts as "actions", and the texts associated with actions as "states". We then build Q-networks to learn the policy of extracting actions and extract plans from the labeled texts. We demonstrate the effectiveness of our approach on several datasets with comparison to state-of-the-art approaches, including online experiments interacting with humans.