  In this paper, we propose a novel benchmark called the StarCraft Multi-Agent
Challenges+, where agents learn to perform multi-stage tasks and to use
environmental factors without precise reward functions. The previous challenges
(SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning
are mainly concerned with ensuring that all agents cooperatively eliminate
approaching adversaries only through fine manipulation with obvious reward
functions. This challenge, on the other hand, is interested in the exploration
capability of MARL algorithms to efficiently learn implicit multi-stage tasks
and environmental factors as well as micro-control. This study covers both
offensive and defensive scenarios. In the offensive scenarios, agents must
learn to first find opponents and then eliminate them. The defensive scenarios
require agents to use topographic features. For example, agents need to
position themselves behind protective structures to make it harder for enemies
to attack. We investigate MARL algorithms under SMAC+ and observe that recent
approaches work well in similar settings to the previous challenges, but
misbehave in offensive scenarios. Additionally, we observe that an enhanced
exploration approach has a positive effect on performance but is not able to
completely solve all scenarios. This study proposes new directions for future
research.
