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Counting to Explore and Generalize in Text-based Games
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We propose a recurrent RL agent with an episodic exploration mechanism that helps discovering good policies in text-based game environments. We show promising results on a set of generated text-based games of varying difficulty where the goal is to collect a coin located at the end of a chain of rooms. In contrast to previous text-based RL approaches, we observe that our agent learns policies that generalize to unseen games of greater difficulty.
Forward citations
Cited by 4 Pith papers
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Large Language Models Think Too Fast To Explore Effectively
In Little Alchemy 2, most LLMs discover fewer elements than humans and rely on uncertainty rather than empowerment; reasoning models o1 and DeepSeek-R1 explore more effectively.
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Interactive Language Learning by Question Answering
QAit turns question answering into an interactive text-game task, and the paper's baselines show current agents cannot generalize beyond memorized games, while humans can.
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Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games
Curriculum learning, room-aware action labels, and LinUCB exploration improve zero-shot performance on TextWorld cooking game families, reaching 72% and 68% of achievable points.
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Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation
A thesis proposal repurposing two prior papers on LM agents for text games, framed as a path to theory-of-mind AI, with no new theory-of-mind evidence.
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