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Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge

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arxiv 2005.00811 v1 pith:DVWILF4P submitted 2020-05-02 cs.AI cs.CLcs.LG

Keerthiram Murugesan , Mattia Atzeni , Pushkar Shukla , Mrinmaya Sachan , Pavan Kapanipathi , Kartik Talamadupula This is my paper
classification cs.AIcs.CLcs.LG
keywords agentsenvironmentsknowledgetext-basedcommonsenselearningperformancereinforcement
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In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments. This reliance on text brings advances in natural language processing into the ambit of these agents, with a recurring thread being the use of external knowledge to mimic and better human-level performance. We present one such instantiation of agents that use commonsense knowledge from ConceptNet to show promising performance on two text-based environments.

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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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