# Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning

arXiv: https://arxiv.org/abs/1910.04040 · HTML: https://mlsanity.com/paper/1910.04040/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2208.01573/) (2022)
- [CLIP-RL: Aligning Language and Policy Representations for Task Transfer in Reinforcement Learning](https://mlsanity.com/paper/2512.01616/) (2025)

## Abstract (arXiv)

Over its lifetime, a reinforcement learning agent is often tasked with different tasks. How to efficiently adapt a previously learned control policy from one task to another, remains an open research question. In this paper, we investigate how instructions formulated in natural language can enable faster and more effective task adaptation. This can serve as the basis for developing language instructed skills, which can be used in a lifelong learning setting. Our method is capable of assessing, given a set of developed base control policies, which policy will adapt best to a new unseen task.