How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds (2021.naacl-main)
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| Challenge: | a recent improvement in the quality of natural language processing and generation (NLG) is needed for goal-oriented ML driven agents. |
| Approach: | They propose a reinforcement learning system that integrates large-scale language modeling and commonsense reasoning-based pre-training to imbue the agent with relevant priors. |
| Outcome: | The proposed system is able to act and talk naturally with respect to their motivations. |
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Learning to Speak and Act in a Fantasy Text Adventure Game (D19-1)
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Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
| Challenge: | Existing studies on grounded dialogue use only statistical regularities of text data, without explicit understanding of the world that the text describes. |
| Approach: | They propose a large-scale crowdsourced text adventure game as a research platform for studying grounded dialogue. |
| Outcome: | The proposed game allows agents to perceive, emote, and act whilst conducting dialogue with other agents. |
Situated Dialogue Learning through Procedural Environment Generation (2022.acl-long)
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| Challenge: | a key hypothesis in the pursuit towards creating goal-driven natural language-based agents is interactivity and environment grounding is critical for effective language learning. |
| Approach: | They augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents. |
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I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons (2023.acl-long)
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Pei Zhou, Andrew Zhu, Jennifer Hu, Jay Pujara, Xiang Ren, Chris Callison-Burch, Yejin Choi, Prithviraj Ammanabrolu
| Challenge: | Existing dialogue agents, while able to produce human-like responses, often do not model goal-driven and grounded language interactions. |
| Approach: | They propose to decompose and model teacher-student natural language interactions into (1) the DM’s intent to guide players toward a given goal; (2) the dm’s guidance utterance to the players expressing this intent; (3) a theory-of-mind model that anticipates the players’ reaction to the guidance one turn into the future. |
| Outcome: | The proposed task is based on a goal-driven and grounded environment with a teacher-student interaction model and theory-of-mind model. |
Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts (N18-2)
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| Challenge: | a novel approach to understanding narratives involves modelling the interaction between characters and actions . we propose role-playing games as a testbed for inferring interactions between characters in narratives . |
| Approach: | They propose role-playing games as a testbed for learning latent ties between characters and actions . they propose to combine character and action descriptions from online discussion forums . |
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Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning (2020.acl-main)
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| Challenge: | a new method for combining multi-agent communication with traditional data-driven approaches to natural language learning is proposed . we combine the two types of learning with a goal of teaching agents to communicate with humans in natural language. |
| Approach: | They propose a method that combines traditional data-driven approaches to natural language learning with multi-agent self-play environments. |
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STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language Models (2024.findings-acl)
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| Challenge: | Existing environments for text-based RL are domain-specific or time-consuming to generate and do not train the agents to master a specific set of skills. |
| Approach: | They propose an interactive environment for self-supervised RL that bootstraps the text-based RL agents with automatically generated games to boost their generalization capabilities. |
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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. |
| Outcome: | The proposed solutions are based on the proposed interactive fiction games and the generated environments. |
Reading and Acting while Blindfolded: The Need for Semantics in Text Game Agents (2021.naacl-main)
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| Challenge: | Recent work has used text-based games as a testbed for developing autonomous agents that operate using natural language. |
| Approach: | They propose an inverse dynamics decoder to regularize representation space and encourage exploration to reduce the amount of semantic information available to a learning agent. |
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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. |
| Outcome: | The proposed model performs well in multiple challenging games. |
Seeded self-play for language learning (D19-64)
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| Challenge: | Current methods for learning human language are too data inefficient to learn it in this way. |
| Approach: | They propose to train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. |
| Outcome: | The proposed algorithm minimizes the number of on-policy interactions while learning human language while minimizing the number on-political interactions. |