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Counting to Explore and Generalize in Text-based Games

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arxiv 1806.11525 v2 pith:KRVL4OZO submitted 2018-06-29 cs.CL cs.LG

Xingdi Yuan , Marc-Alexandre Côté , Alessandro Sordoni , Romain Laroche , Remi Tachet des Combes , Matthew Hausknecht , Adam Trischler This is my paper
classification cs.CLcs.LG
keywords text-basedgamesagentdifficultygeneralizepoliciesapproacheschain
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-Agent Language Models: Advancing Cooperation, Coordination, and Adaptation

    cs.CL 2025-06 conditional novelty 3.0 of 10

    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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