# Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTS

arXiv: https://arxiv.org/abs/1910.12134 · HTML: https://mlsanity.com/paper/1910.12134/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2019.

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

- [Generalising Discrete Action Spaces with Conditional Action Trees](https://mlsanity.com/paper/2104.07294/) (2021)
- [Online Reinforcement Learning-Based Dynamic Adaptive Evaluation Function for Real-Time Strategy Tasks](https://mlsanity.com/paper/2501.03824/) (2025)

## Abstract (arXiv)

This paper presents a preliminary study comparing different observation and action space representations for Deep Reinforcement Learning (DRL) in the context of Real-time Strategy (RTS) games. Specifically, we compare two representations: (1) a global representation where the observation represents the whole game state, and the RL agent needs to choose which unit to issue actions to, and which actions to execute; and (2) a local representation where the observation is represented from the point of view of an individual unit, and the RL agent picks actions for each unit independently. We evaluate these representations in μRTS showing that the local representation seems to outperform the global representation when training agents with the task of harvesting resources.