| Challenge: | Text adventure games provide a stepping stone toward grounding action in language . prior work demonstrated that using a knowledge graph as a state representation facilitates faster control policy learning. |
| Approach: | They propose to use knowledge graphs as a representation for domain knowledge transfer for training text-adventure playing reinforcement learning agents. |
| Outcome: | The proposed methods let us learn a higher-quality control policy faster in text adventure games. |
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| Challenge: | Text adventure games provide a platform for exploring reinforcement learning in combinatorial action space, such as natural language. |
| Approach: | They propose a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. |
| Outcome: | The proposed architecture can learn a control policy faster than baseline alternatives. |
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations (2021.acl-short)
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Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell
| Challenge: | Text-based games (TBGs) are useful benchmarks for evaluating progress in grounded language understanding and reinforcement learning (RL). |
| Approach: | They propose an agent that induces a graph representation of the game state and jointly grounds it with a commonsense knowledge from ConceptNet. |
| Outcome: | The proposed agent outperforms baseline agents in the proposed game . |
Generalization in Text-based Games via Hierarchical Reinforcement Learning (2021.findings-emnlp)
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| Challenge: | Reinforcement Learning (RL) based agents are promising for text-based games, but their generalization remains a challenge. |
| Approach: | They propose a hierarchical framework for reinforcement learning based on knowledge graphs . they propose to decompose the game into subtasks and execute a sub-policy in the low level to conduct goal-conditioned reinforcement learning. |
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DeepTrans: Deep Reasoning Translation via Reinforcement Learning (2026.tacl-1)
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| Challenge: | Recent studies have shown promising performance in various downstream tasks. |
| Approach: | They propose a deep reasoning translation model that learns free translation via reinforcement learning (RL) they build a reward model with pre-defined scoring criteria on the translation results and thought processes . |
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Natural Language-based State Representation in Deep Reinforcement Learning (2024.findings-naacl)
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| Challenge: | a new method for learning policies from images is proposed to reduce image-based observations' complexity and improve interpretability. |
| Approach: | They propose a method that compresses images into a natural language form for state representation. |
| Outcome: | The proposed method allows better interpretability and leverages processing capabilities of large-language models. |
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games (2022.acl-long)
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| Challenge: | Text-based games provide an interactive way to study natural language processing. |
| Approach: | They propose a two-phase training framework to decouple language learning from reinforcement learning and improve the sample efficiency. |
| Outcome: | The proposed method significantly improves performance and sample efficiency against compound error and limited pre-training data. |
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
| Outcome: | The proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. |
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning (2024.lrec-main)
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| Challenge: | General pre-trained language models (PLMs) leverage relation triples from knowledge graphs (KGs) and integrate external data sources into language models via self-supervised learning. |
| Approach: | They propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL) to detect positions for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
| Outcome: | The proposed model can detect essential positions in texts for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
A Survey of Text Games for Reinforcement Learning Informed by Natural Language (2022.tacl-1)
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| Challenge: | Interactive Fiction Games (text games) are a problem type that require natural language to solve complex tasks. |
| Approach: | They propose to use interactive fiction games as a testing environment to test the new Reinforcement Learning solutions using natural language. |
| Outcome: | The proposed solutions are based on the proposed interactive fiction games and the generated environments. |
Deep Reinforcement Learning-based Dialogue Policy with Graph Convolutional Q-network (2024.lrec-main)
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| Challenge: | Existing methods for deep reinforcement learning lack the ability to learn the relationship between dialogue states and actions. |
| Approach: | They propose a graph-structured dialogue policy framework for task-oriented dialogue systems that uses bipartite graphs to construct two different bipartites and generate user-related and knowledge-related subgraphs. |
| Outcome: | The proposed framework significantly improves the effectiveness and stability of dialogue policies. |