Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning (N19-1)
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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. |
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Transfer in Deep Reinforcement Learning Using Knowledge Graphs (D19-53)
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| 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. |
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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. |
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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. |
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Abstract then Play: A Skill-centric Reinforcement Learning Framework for Text-based Games (2023.findings-acl)
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| Challenge: | Existing reinforcement learning frameworks fail to decompose the task and abstract the action autonomously. |
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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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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 . |
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning (2024.eacl-long)
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Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell, Kartik Talamadupula, Tim Klinger
| Challenge: | Text-based games (TBGs) combine natural language understanding with reasoning. |
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Self-imitation Learning for Action Generation in Text-based Games (2023.eacl-main)
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| Challenge: | Text-based games are situated systems where the game agents observe textual descriptions, and generate textual commands to interact with the environment. |
| Approach: | They propose a confidence-based self-imitation model to generate action candidates for the RL agent by exploiting past valuable trajectories to adapt a pre-trained language model towards a target game. |
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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. |
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games (2022.acl-short)
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| Challenge: | Existing RL agents are far away from solving text-based games due to their combinatorially large action spaces that hinders efficient exploration. |
| Approach: | They propose an exploration technique that injects external commonsense knowledge, via a pretrained language model, into the agent during training when the agent is the most uncertain about its next action. |
| Outcome: | The proposed method exhibits improvement on the collected game scores during the training in four out of nine games from Jericho. |