Grounding Language for Transfer in Deep Reinforcement Learning

TL;DR

This paper introduces a method that uses natural language descriptions to improve transfer learning in deep reinforcement learning, enabling agents to adapt more effectively across different environments.

Contribution

The paper proposes a novel approach that grounds textual environment descriptions to facilitate transfer in deep RL, outperforming prior models in various scenarios.

Findings

Achieved up to 14% improvement in average rewards.

Achieved up to 11.5% improvement in initial rewards.

Outperformed prior work in transfer and multi-task environments.

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.