Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS
This is an incremental study for researchers in reinforcement learning and game AI, focusing on representation choices in a specific domain.
The paper tackled the problem of comparing observation and action space representations for Deep Reinforcement Learning in Real-time Strategy games, specifically in μRTS, and found that the local representation outperformed the global representation in resource harvesting tasks.
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 $μ$RTS showing that the local representation seems to outperform the global representation when training agents with the task of harvesting resources.