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Grounding language for transfer in deep reinforcement learning

Author(s): Narasimhan, Karthik; Barzilay, R; Jaakkola, T

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Abstract: © 2018 AI Access Foundation. All rights reserved. In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning generalized policy representations that work across domains remains a challenging problem. We demonstrate that textual descriptions of environments provide a compact intermediate channel to facilitate effective policy transfer. Specifically, by learning to ground the meaning of text to the dynamics of the environment such as transitions and rewards, an autonomous agent can effectively bootstrap policy learning on a new domain given its description. We employ a model-based RL approach consisting of a differentiable planning module, a model-free component and a factorized state representation to effectively use entity descriptions. Our model outperforms prior work on both transfer and multi-task scenarios in a variety of different environments. For instance, we achieve up to 14% and 11.5% absolute improvement over previously existing models in terms of average and initial rewards, respectively.
Publication Date: 1-Dec-2018
Citation: Narasimhan, K, Barzilay, R, Jaakkola, T. (2018). Grounding language for transfer in deep reinforcement learning. Journal of Artificial Intelligence Research, 63 (849 - 874. doi:10.1613/jair.1.11263
DOI: doi:10.1613/jair.1.11263
EISSN: 1076-9757
Pages: 849 - 874
Type of Material: Journal Article
Journal/Proceeding Title: Journal of Artificial Intelligence Research
Version: Final published version. This is an open access article.

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