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Preprint
Comparing Observation and Action Representations for Deep Reinforcement Learning in $\mu$RTS
ArXiv.org
26 Oct 2019
Abstract
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 $\mu$RTS showing that the local representation seems to
outperform the global representation when training agents with the task of
harvesting resources.
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Details
- Title
- Comparing Observation and Action Representations for Deep Reinforcement Learning in $\mu$RTS
- Creators
- Shengyi HuangSantiago Ontañón
- Publication Details
- ArXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Other Identifier
- 991021869012104721