# Reusability and Transferability of Macro Actions for Reinforcement Learning

arXiv: https://arxiv.org/abs/1908.01478 · HTML: https://mlsanity.com/paper/1908.01478/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

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

- [Action abstractions for amortized sampling](https://mlsanity.com/paper/2410.15184/) (2024)
- [Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control](https://mlsanity.com/paper/2410.08979/) (2024)

## Abstract (arXiv)

Conventional reinforcement learning (RL) typically determines an appropriate primitive action at each timestep. However, by using a proper macro action, defined as a sequence of primitive actions, an agent is able to bypass intermediate states to a farther state and facilitate its learning procedure. The problem we would like to investigate is what associated beneficial properties that macro actions may possess. In this paper, we unveil the properties of reusability and transferability of macro actions. The first property, reusability, means that a macro action generated along with one RL method can be reused by another RL method for training, while the second one, transferability, means that a macro action can be utilized for training agents in similar environments with different reward settings. In our experiments, we first generate macro actions along with RL methods. We then provide a set of analyses to reveal the properties of reusability and transferability of the generated macro actions.