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A fair comparison of strategy-game AI agents requires more than a headline win rate. Evaluate them on a declared, repeatable set of scenarios and opponents; disclose the game conditions, interfaces, constraints, and resource budgets; and show results for individual matchups as well as any aggregate. The right benchmark depends on the claim: full games test a broad mix of abilities, while focused scenarios help diagnose particular skills.
Why a single win rate is not enough
A win rate describes an agent’s results only against the opponents, maps, rules, and conditions used to measure it. Change those conditions and the result may change too. In particular, agents can interact non-transitively: one may beat a second, the second may beat a third, and the third may exploit the first. The AlphaStar study reported such non-transitive interactions among agents and exploiters, so success against one opponent cannot establish a universal ranking. Vinyals et al., Nature, 2019
Results should therefore make the evaluation population visible. A pooled score can be useful, but without scenario- and opponent-level results it can hide where an agent succeeds or fails. Uriarte and Ontañón designed StarCraft scenarios and metrics to make comparisons more systematic; they describe the goal as a finer-grained view of strengths and weaknesses, not a replacement for competition as a test of complete agents. Benchmark paper Paper PDF
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Before choosing maps or opponents, state what the result is meant to establish: tactical micro, strategic planning, robustness to varied conditions, performance against humans, or generality across game conditions. No single evaluation format answers all of these questions.
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| Evaluation setting | What it can show | Main limitation |
|---|---|---|
| Full game or ladder-style matches | Broad performance across the interacting demands of realistic play. | Many skills and conditions interact; a narrow map set or opponent pool can distort the picture. DeepMind, 2017 Vinyals et al., 2019 |
| Scenario benchmark | More diagnostic comparisons across defined RTS tasks. | What it reveals depends on scenario selection, and it does not by itself substitute for competition between complete agents. Uriarte and Ontañón, 2015 Paper PDF |
| Mini-games or other focused suites | Evidence about selected capabilities, such as a particular gameplay element. | Success on a focused task does not establish full-game strength. StarCraft II: A New Challenge for Reinforcement Learning Two-Bridge Map Suite preprint |
StarCraft II makes this distinction especially important: it combines multiple agents, partial observation, a large state and action space, and delayed credit assignment. Mini-games can isolate some of these challenges, but the result then describes those selected tasks rather than the whole game. Vinyals et al., 2017
A repeatable evaluation protocol
- Define the claim. Identify the capability or generality being evaluated. Select full games, scenarios, or both to suit that claim, and state what the chosen tests cannot show.
- Freeze and document the environment. Record the game build and rules, map and scenario versions, faction or matchup assignment, interface or API, observation access, action constraints, and any game modifications. These details make it possible to interpret a result and reproduce its conditions. StarCraft II was opened as a research environment through SC2LE; the launch announcement emphasized the value of testing in established games where humans play well. DeepMind, 2017
- Use a declared set of scenarios. Explain why those maps or tasks fit the target claim. Report each scenario separately before presenting a pooled result, so a strong aggregate cannot conceal a weak area.
- Use a declared opponent set. Identify whether opponents are built-in bots, fixed scripts, self-play versions, a league, or humans; give their identities or explain the selection procedure. Where feasible, include different strategy styles and report each matchup. This makes opponent-dependent strengths and exploitable weaknesses easier to see. Vinyals et al., 2019
- Make randomness and uncertainty inspectable. State the random seeds, number of games, and aggregation method. Show enough per-scenario and per-opponent detail for readers to see how results vary. There is no universal sample count or confidence-interval method established for every evaluation in the sources cited here, so do not imply that one number guarantees a fair test.
- Disclose resource and action budgets. Report relevant training and inference resources and action-rate constraints, especially when comparing agents with different budgets. Treat these as part of the comparison design: no single cross-agent budget is established here as a universal standard.
- Preserve reproducibility artifacts. Where possible, publish configurations, map files, agent versions, replays, and evaluation scripts. The AlphaStar paper states that its online games and raw Battle.net experiment data were made available as supplementary data. Vinyals et al., 2019
Interpret focused benchmarks within their scope
A focused test is useful when it isolates a capability that a full game would entangle with many others. But its score should not be presented as a general measure of strategic-game ability. For example, the Two-Bridge Map Suite described in a 2026 preprint disables economy and fog of war to focus on navigation and micro-combat. That is a recent proposal with preliminary experiments, not settled consensus or evidence of full-game performance. Panda et al., 2026 preprint
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Conversely, full-game play provides broader evidence but is harder to diagnose: a loss might reflect a tactical mistake, strategic planning, map-specific weakness, or another interacting factor. A strong evaluation can pair broad matches with focused scenarios, label the purpose of each, and avoid treating either as a substitute for the other.
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Evaluation context matters even for prominent results. In its 2019 Nature paper, AlphaStar’s authors reported Grandmaster level for all three StarCraft II races and performance above 99.8% of officially ranked human players in that study. This is a finding from that evaluation, not a current ladder estimate or a general benchmark for other agents. Nature paper, 30 October 2019
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Likewise, the fact that a result comes from a benchmark does not make it universally fair. Its meaning depends on the chosen scenarios, maps, opponents, interfaces, and constraints. Comparisons are strongest when those choices are explicit, results are broken out before aggregation, and the conclusion stays within the tested scope.
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