Grounding Language for Transfer in Deep Reinforcement Learning

Abstract

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.

JAIR 2018

References in corpus (5)

• Massively Parallel Methods for Deep Reinforcement Learning
• Modular Multitask Reinforcement Learning with Policy Sketches
• Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
• Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
• Joint Dictionaries for Zero-Shot Learning

Cited by in corpus (5)

• Investigating Human Priors for Playing Video Games
• Internet Congestion Control via Deep Reinforcement Learning
• Zero-Shot Compositional Policy Learning via Language Grounding
• Learning to Play General Video-Games via an Object Embedding Network
• Feudal Reinforcement Learning by Reading Manuals