# Guided Policy Search for Parameterized Skills using Adverbs

arXiv: https://arxiv.org/abs/2110.15799 · HTML: https://mlsanity.com/paper/2110.15799/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 23 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Modular Multitask Reinforcement Learning with Policy Sketches](https://mlsanity.com/paper/1611.01796/) (2016)
- [A Survey of Reinforcement Learning Informed by Natural Language](https://mlsanity.com/paper/1906.03926/) (2019)
- [Using Natural Language for Reward Shaping in Reinforcement Learning](https://mlsanity.com/paper/1903.02020/) (2019)
- [Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning](https://mlsanity.com/paper/1706.05064/) (2017)
- [Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning](https://mlsanity.com/paper/1712.07294/) (2017)
- [Reinforcement Learning with Parameterized Actions](https://mlsanity.com/paper/1509.01644/) (2015)
- [Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences](https://mlsanity.com/paper/1506.04089/) (2015)
- [Hierarchical Decision Making by Generating and Following Natural Language Instructions](https://mlsanity.com/paper/1906.00744/) (2019)
- [Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities](https://mlsanity.com/paper/1704.06616/) (2017)
- [Bootstrapping Motor Skill Learning with Motion Planning](https://mlsanity.com/paper/2101.04736/) (2021)

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

- [Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications](https://mlsanity.com/paper/2501.18848/) (2025)
- [Skill Generalization with Verbs](https://mlsanity.com/paper/2410.14118/) (2024)

## Abstract (arXiv)

We present a method for using adverb phrases to adjust skill parameters via learned adverb-skill groundings. These groundings allow an agent to use adverb feedback provided by a human to directly update a skill policy, in a manner similar to traditional local policy search methods. We show that our method can be used as a drop-in replacement for these policy search methods when dense reward from the environment is not available but human language feedback is. We demonstrate improved sample efficiency over modern policy search methods in two experiments.