The Tool Desk
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How do I make a StarCraft II AI bot?
Start by choosing how much of the game interface you want to manage. For a Python-first project, BurnySc2/python-sc2 is a higher-level wrapper; its introduction lists Python 3.9 or newer and a StarCraft II installation as requirements. For a lower-level route, Blizzard provides protocol definitions and the C++ s2client-api. Blizzard’s repository also links to PySC2.
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| Route | What it is for | What to keep in mind |
|---|---|---|
| BurnySc2/python-sc2 | Python bot development through a wrapper around the game interface. | Requires Python 3.9 or newer and an SC2 installation, according to its documentation. |
Blizzard protocol and s2client-api |
Client-level control using Blizzard’s protocol definitions and C++ library. | Offers a lower-level interface than a high-level bot wrapper. |
s2protocol |
Decoding replay structures and events for offline analysis or data preparation. | It is a low-level parser, not a live-game control library or high-level analysis pipeline. |
Blizzard describes the API in its s2client-proto repository as an interface for external control of StarCraft II. The protocol uses protobuf definitions, and Blizzard’s C++ client library communicates with the game over WebSocket. The API is intended for scripted and machine-learning bots, replay analysis, and tool-assisted human play.
Build the smallest useful policy first
Before attempting complex reinforcement learning, make a basic policy complete the observation/action cycle. A fixed opening or a small rule-based policy is enough to expose integration problems: whether the client starts, the agent receives observations, issued actions are accepted, and the match reaches an outcome.
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Log each game’s observations, actions, invalid actions, result, map, race matchup, and game version. This record makes it possible to distinguish a weak strategy from a broken action or a version mismatch. It is a recommended development practice, not a claim that any particular policy has been tested here.
How do I use the StarCraft II API?
The live API is a repeated control loop between your agent and a running SC2 client. Blizzard’s protocol documentation describes the core sequence as launching the client, creating or joining a game, requesting an observation, processing it, sending actions, and repeating until the game ends.
- Launch the game client. Start the SC2 process your agent will control.
- Create or join a game. Set up the match and connect the agent to it.
- Request an observation. Read the game state exposed through the API.
- Choose and send actions. Convert the observation into one or more commands.
- Check the response and observe again. Continue the loop until the match ends, recording failures and the final result.
In step mode, clients synchronize their progress rather than advancing independently. That can be useful for controlled experiments, but it is a runtime choice—not a replay-training interface.
Do not assume an issued command succeeded
The protocol documentation warns that an action can fail validation immediately or fail later during execution. Inspect action responses and subsequent observations for evidence that the intended command took effect. If an action does not produce the expected state change, record it as a failure rather than treating it as a successful decision in training or evaluation data.
Can I train a bot from StarCraft II ladder replays?
Yes, but replays provide game records, not a complete supervised-training pipeline. Blizzard’s project resources link to 1v1 ladder replay packs and map packs. Access to Linux packages, maps, and replay packs requires agreement to Blizzard’s AI and Machine Learning License, as stated on its project resources page.
Blizzard’s s2protocol decodes replay structures and events, including headers, game details, initialization data, game events, message events, and tracker events. Its documentation presents the parser as a first step for data mining; it does not supply high-level analysis or game-balance interpretation.
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Turn replay events into a learning problem
A replay contains information about what happened in a game. To use it for learning, you still need to decide what the model sees, what it should predict, and how replay events correspond to the actions available through your live agent’s interface. Plan the data pipeline explicitly:
- Define observations: select the state features available to the model and how to represent them.
- Choose targets: decide whether to predict player actions, strategic choices, outcomes, or another label.
- Filter and normalize: establish which replays and events qualify and how values are made consistent.
- Align representations: map replay events to the action representation your agent can actually issue.
- Keep provenance: retain replay, map, client-version, and parser-version information with each processed example.
Replay decoding and live interaction are therefore complementary but distinct jobs: one extracts recorded events offline; the other exchanges observations and actions with an active game.
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How do I parse replays reproducibly?
SC2 replay playback re-simulates the recorded inputs. Correct playback depends on matching the patch, game binary, data version, and map dependencies. Blizzard’s protocol documentation notes that a replay-info request can report version values, which can help identify the versions associated with a replay.
Map dependency handling differs by platform: the documentation says ladder replay dependencies may download automatically on Windows and Mac, but explicitly excludes Linux from that behavior. Do not assume a Linux setup will fetch a required map for you.
Keep experiment metadata together
Store the replay files alongside the maps and identifiers needed to reproduce their interpretation: client build and data version, parser version, and any preprocessing configuration. If playback or event extraction changes across versions, this record helps identify whether a difference comes from the bot or the underlying game data.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I evaluate the bot?
Evaluate in stages. Begin with controlled offline matches to verify that the agent can finish games and to establish a baseline. Then test across repeated maps and matchups, keeping the opponent type and game version consistent enough for comparisons to mean something.
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Report what the result actually measures
For a useful comparison, record the win rate together with its sample size, opponent type or strength, maps, game version, and race matchup. Identify whether the games were against built-in AI, a community bot ladder, or human opponents. Raw win rates from different settings should not be compared without explaining those differences.
The SC2LE paper describes a particular research environment using preprocessed observations, a simplified action space, lock-step execution, and full games against built-in AI. In the experiments described in that paper, its baseline agents did not learn to beat the easiest built-in AI. That is a result about those agents and that setup—not evidence about every later method, current game version, or human-ladder performance.
Can I put my bot on the StarCraft II ladder?
Blizzard’s repository lists SC2AI and AI Arena as unofficial, community-run bot ladders. The reviewed project pages do not establish whether either currently accepts submissions or what its current entry rules are. Check the organizer’s current instructions before planning around a particular ladder.
Blizzard’s replay packs are a documented source of ladder game data, but the reviewed sources do not confirm a currently available official Battle.net player-profile or ladder endpoint for collecting live ladder statistics. Treat replay-pack data and live profile data as separate questions; do not assume the former implies the latter.
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What the SC2LE research setup does—and does not—show
The SC2LE work connected a Python interface with human replay data collected from ranked games through Blizzard’s official Battle.net ladder. Its environment also used simplifications that matter when interpreting results: preprocessed observations, a reduced action space, lock-step execution, and built-in-AI opponents. Those choices made research experiments possible, but they do not make a model trained or evaluated there automatically ready for the human Battle.net ladder.
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