Guided Policy Search for Parameterized Skills using Adverbs
arXiv:2110.15799
Abstract
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.
arXiv comment7 pages, 3 figures
References in corpus (7)
- Modular Multitask Reinforcement Learning with Policy Sketches
- Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
- Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
- Learning Parameterized Skills
- Reinforcement Learning with Parameterized Actions
- Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning
- Hierarchical Decision Making by Generating and Following Natural Language Instructions