The Tool Desk
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What claim is the evaluation meant to support?
Start by defining the capability you want to measure. A test of tactical micro, long-term planning, robustness to different opponents, or performance against human players needs a benchmark suited to that claim. A single score cannot distinguish those abilities when they are entangled in a full match.
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That distinction matters especially in StarCraft II, which combines multiple agents, partial observation, a large state and action space, and delayed credit assignment. Its research environment includes focused mini-games that isolate selected parts of gameplay; those tasks can help diagnose a capability, but they do not establish full-game strength. See StarCraft II: A New Challenge for Reinforcement Learning.
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Choose between broad competition and diagnostic tests
Full-game matches and focused benchmarks answer different questions. A full game tests a broad bundle of skills under interacting conditions. A scenario suite can show where an agent performs well or poorly across specific tasks. Neither format is universally best: choose according to the claim, and state what the chosen setting leaves out.
#1 Best Overall
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| Evaluation setting | What it can show | Main limitation |
|---|---|---|
| Full game or ladder-style matches | Broad performance in a complex game under realistic play conditions. | Skills and conditions interact; a narrow map set or single opponent can make the result misleading. See the 2017 StarCraft II research-environment announcement and the 2019 AlphaStar study. |
| Scenario benchmark | More diagnostic strengths and weaknesses across defined RTS tasks. | Coverage depends on scenario selection, and benchmark results alone do not replace competition between complete agents. Uriarte and Ontañón describe their benchmark as a way to provide a finer-grained picture of algorithms, techniques, and agents. See the 2015 benchmark paper and its PDF. |
| Mini-games or focused suites | Selected capabilities, such as micro-combat or navigation, with fewer full-game interactions. | Success on a narrow task does not establish full-game ability. The SC2 research-environment paper discusses focused mini-games; a 2026 Two-Bridge Map Suite preprint proposes disabling economy and fog of war to focus on navigation and micro-combat. |
The Two-Bridge work is a recent preprint with preliminary experiments, not an established standard. Treat its results as evidence about that proposed task, not as a general ranking of StarCraft agents.
Use a repeatable reporting protocol
The following checklist is a practical synthesis of benchmark and environment work, not a universal standard. The sources do not establish one required compute budget, sample size, or statistical method for every game.
Rank #2
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- State the target claim. Name the capability and the population you intend to generalize to—for example, performance on specified tactical scenarios or against a defined set of opponents.
- Freeze and identify the environment. Record the game and version, rules or modifications, map and scenario versions, faction or matchup assignments, and interface or API. Describe what the agent can observe and which actions it can take, including any constraints. StarCraft II was opened as a research environment, while its creators argued that testing in established games where humans play well can make benchmark performance more meaningful; see the 2017 announcement.
- Declare the scenario set. List the maps or tasks, explain why they fit the target claim, and report results for each before presenting a pooled score. Scenario-based evaluation is intended to expose differences that an aggregate can hide; the StarCraft agent benchmark was designed to capture distinct aspects of RTS play.
- Define the opponent population. Identify whether opponents are built-in bots, fixed scripts, self-play versions, a league, or human players. Give opponent identities or the selection procedure, and include varied styles where feasible. Report matchup-level outcomes rather than treating one opponent as representative of all play.
- Make randomness and aggregation visible. Disclose seeds, game counts, how matches were assigned, and how any aggregate was calculated. Report uncertainty in a way appropriate to the experiment. There is no source-backed universal number of games or prescribed confidence-interval method for all strategy-game evaluations, so do not present one as a general threshold.
- Account for resources and action rates. State relevant training and inference compute, time limits, and action-rate constraints when comparing agents that had different budgets. These are fairness disclosures, not a single cross-game budget established by the cited work.
- Preserve the materials needed to repeat the test. Where possible, release or archive agent versions, configuration, maps, evaluation scripts, and replay data. The AlphaStar paper reported making online games and raw Battle.net experiment data available as supplementary data.
Why opponent choice can change the ranking
Win rate is always conditional on the opponents and scenarios used to measure it. Strategy interactions can be non-transitive: an agent that beats one style may lose to another, while a third agent may reverse those outcomes. The AlphaStar study reported highly non-transitive interactions among agents and exploiters, illustrating why results against a single opponent cannot support a universal ranking. See Vinyals et al., Nature, 2019.
For a robust comparison, show the matchup matrix or per-opponent results when practical. If a league or sampled opponent pool is used, explain how members were selected and whether the same pool was used for every agent. The aggregate can still be useful, but its meaning depends on that population and its weighting.
Rank #3
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- EASY TO LEARN, CHALLENGING TO MASTER: Enjoy a game that's easy to learn but filled with strategic depth. Face off against your opponent, strengthen your decks, and vie for victory.
- CHOOSE YOUR SIDE: Play as either the Empire or the Rebels, each with its own unique playstyle and thematic abilities. Customize your strategy as you aim to destroy your opponent's bases.
- ICONIC STAR WARS CHARACTERS: Over 50 different cards allow you to take command of your favorite Star Wars characters, vehicles, and starships. Deploy iconic bases like the Death Star and Hoth to gain powerful abilities.
- THRILLING GALACTIC CONFLICT: Engage in intense head-to-head battles that bring the Galactic Empire and Rebel Alliance to life on your tabletop. Be the first to destroy three of your opponent's bases to claim victory.
How to interpret a headline achievement
AlphaStar is a useful example of why a performance number needs its study context. The authors reported Grandmaster level for all three races and performance above 99.8% of officially ranked human players in the 2019 study. This is a result from that evaluation—not a current ladder estimate or a general measure of all StarCraft agents. The study’s headline cannot be separated from its game conditions, human comparison population, and evaluation design. Read the Nature paper for the reported scope.
Quick Recap
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