Grounding language for transfer in deep reinforcement learning

Karthik Narasimhan, Regina Barzilay, Tommi Jaakkola

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)849-874
Number of pages26
JournalJournal of Artificial Intelligence Research
StatePublished - Dec 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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