This allows actor.Info.HasTraitInfo to be used when checking if an actor needs to be added to the index, which is a cheaper call than actor.TraitsImplementing.
When HarvesterBotModule is ordering idle harvesters to nearby resources, it previously scanned from the harvester's current location. Instead, it now scans from the location of the nearest refinery. As the harvester will likely make many runs between the resource and the refinery, it is better to choose a location near the refinery. This will minimise overall distance travelled to harvest the resource patch.
The AI uses HarvesterBotModule to check for idle harvesters, and give them harvest orders. By default it scans every 50 ticks (2 seconds at normal speed), and for any idle harvesters locates an ore patch and issues a harvest order. This FindNextResource to scan for a suitable ore path is quite expensive. If the AI has to scan for ore patches for several harvesters, then this can produce a noticeable lag spike. Additionally, when there are no available ore patches, the scan will just keep repeating since the harvesters will always be idle - thus the lag spikes repeat every 50 ticks.
To reduce the impact, there already exists a randomization on the first scan interval so that multiple different AIs scan on different ticks. By ensuring the AI players scan at different times, we avoid a huge lag spike where they all operate on the same tick.
To reduce the impact even more, we make four additional changes:
- Scans continue to be done every 50 ticks to detect harvesters. But we spread out the searches for ore patches over multiple later ticks. We'll only perform one ore patch search per tick. This means instead of ordering e.g. 30 harvesters on a single tick and creating a spike, we order one on each tick over the next 30 ticks instead. This spreads out the performance impact.
- When a harvester fails to locate any suitable ore patch, we put it on a longer cooldown, by default 5x the regular cooldown. We don't need to scan as often for these harvesters, since it'll take time for new resources to appear.
- We change the path search in FindNextResource from FindPathToTargetCellByPredicate to FindPathToTargetCells. The format in an undirected path search that must flood fill from the start location. If ore is on the other side of the map, this entails searching the whole map which is very expensive. By maintaining a lookup of resource types per cell, we can instead give the target locations directly to the path search. This lookup requires a small overhead to maintain, but allows for a far more efficient path search to be carried out. The search can be directed towards the target locations, and the hierarchical path finder can be employed resulting in a path search that explores far fewer cells. A few tweaks are made to ResourceClaimLayer to avoid it creating empty list entries when this can be avoided.
- We adjust how the enemy avoidance cost is done. Previously, this search used world.FindActorsInCircle to check for nearby enemies, but this check was done for every cell that was searched, and is itself quite expensive. Now, we create a series of "bins" and cache the additional cost for that bin. This is a less fine grained approach but is sufficient for our intended goal of "avoid resource patches with too many enemies nearby". The customCost function is now less expensive so we can reuse the avoidance cost stored for each bin, rather than calculating fresh for every cell.
- AI places rally points at pathable locations. A pathable location isn't strictly required, but avoids the AI setting rally points at seemingly dumb locations. This is an addtional check on top of the existing buildability check.
- AI now evaluates rally points every AssignRallyPointsInterval (default 100 ticks). Invalid rally points aren't that harmful, so no need to check them every tick. Additionally we do a rolling update so rally points for multiple locations are spread across multiple ticks to reduce any potential lag spikes.
The AI would often invoke this method inside of loops, searching for a different category of queue each time. This would result in multiple searches against the trait dictionary to locate matching queues. Now we alter the method to create a lookup of all the queues keyed by category. This allows a single trait search to be performed.
UnitBuilderBotModule and BaseBuilderBotModule are updated to fetch this lookup once when required, and pass the results along to avoid calling the method more times than necessary. This improves their performance.
Updating squads is the most expensive part of SquadManagerBotModule. It involves ticking the current squad state. This usually involves finding nearby enemies and evaluating the fuzzy state machine to decide whether to interact with those enemies. Since all the AI squads for a player get ordered on the same tick, this can result in a lag spike.
To reduce the impact, we'll spread out the updates over multiple ticks. This means overall all the AI squads will still be refreshed every interval, but it'll be a rolling update rather than all at once. By spreading out the updates we avoid a lag spike from the cumulative updates of all the squads. Now the lag spike is reduced to the worst any single squad update can incur.
The StateMachine offered a feature to remember the previous state and allow reverting to it. However this feature is unused. Remove it to allow the previous states to be reclaimed by the GC earlier.
Several parts of bot module logic, often through the AIUtils helper class, will query or count over all actors in the world. This is not a fast operation and the AI tends to repeat it often.
Introduce some ActorIndex classes that can maintain an index of actors in the world that match a query based on a mix of actor name, owner or trait. These indexes introduce some overhead to maintain, but allow the queries or counts that bot modules needs to perform to be greatly sped up, as the index means there is a much smaller starting set of actors to consider. This is beneficial to the bot logic as the TraitDictionary index maintained by the world works only in terms of traits and doesn't allow the bot logic to perform a sufficiently selective lookup. This is because the bot logic is usually defined in terms of actor names rather than traits.
This allows the LINQ spelling to be used, but benefits from the performance improvement of the specific methods for these classes that provide the same result.
When handling the Nodes collection in MiniYaml, individual nodes are located via one of two methods:
// Lookup a single key with linear search.
var node = yaml.Nodes.FirstOrDefault(n => n.Key == "SomeKey");
// Convert to dictionary, expecting many key lookups.
var dict = nodes.ToDictionary();
// Lookup a single key in the dictionary.
var node = dict["SomeKey"];
To simplify lookup of individual keys via linear search, provide helper methods NodeWithKeyOrDefault and NodeWithKey. These helpers do the equivalent of Single{OrDefault} searches. Whilst this requires checking the whole list, it provides a useful correctness check. Two duplicated keys in TS yaml are fixed as a result. We can also optimize the helpers to not use LINQ, avoiding allocation of the delegate to search for a key.
Adjust existing code to use either lnear searches or dictionary lookups based on whether it will be resolving many keys. Resolving few keys can be done with linear searches to avoid building a dictionary. Resolving many keys should be done with a dictionary to avoid quaradtic runtime from repeated linear searches.
Ensure the target location can be pathed to by all units in the squad, so the squad won't get stuck if some units can't make it. Improve the choice of leader for the squad. We attempt to a choose a leader whose locomotor is the most restrictive in terms of passable terrain. This maximises the chance that the squad will be able to follow the leader along the path to the target. We also keep this choice of leader as the squad advances, this avoids the squad constantly switching leaders and regrouping backwards in some cases.
Previously, the ClosestTo and PositionClosestTo existed to perform a simple distance based check to choose the closest location from a choice of locations to a single other location. For some functions this is sufficient, but for many functions we want to then move between the locations. If the location selected is in fact unreachable (e.g. on another island) then we would not want to consider it.
We now introduce ClosestToIgnoringPath for checks where we don't care about a path existing, e.g. weapons hitting nearby targets. When we do care about paths, we introduce ClosestToWithPathFrom and ClosestToWithPathTo which will check that a path exists. The PathFrom check will make sure one of the actors from the list can make it to the single target location. The PathTo check will make sure the single actor can make it to one of the target locations. This difference allows us to specify which actor will be doing the moving. This is important as a path might exists for one actor, but not another. Consider two islands with a hovercraft on one and a tank on the other. The hovercraft can path to the tank, but the tank cannot path to the hovercraft.
We also introduce WithPathFrom and WithPathTo. These will perform filtering by checking for valid paths, but won't select the closest location.
By employing the new methods that filter for paths, we fix various behaviour that would cause actors to get confused. Imagine an islands map, by checking for paths we ensure logic will locate reachable locations on the island, rather than considering a location on a nearby island that is physically closer but unreachable. This fixes AI squad automation, and other automated behaviours such as rearming.
1. Only allow new item being queued when cash above a certain number
2. Only tick one kind of queues at one tick, reduce the pressure on the actived tick
3. 'BaseBuilderBotModule' will check all buildings in producing, avoid queue mutiple same buildings.
This changeset is motivated by a simple concept - get rid of the MiniYaml.Clone and MiniYamlNode.Clone methods to avoid deep copying yaml trees during merging. MiniYaml becoming immutable allows the merge function to reuse existing yaml trees rather than cloning them, saving on memory and improving merge performance. On initial loading the YAML for all maps is processed, so this provides a small reduction in initial loading time.
The rest of the changeset is dealing with the change in the exposed API surface. Some With* helper methods are introduced to allow creating new YAML from existing YAML. Areas of code that generated small amounts of YAML are able to transition directly to the immutable model without too much ceremony. Some use cases are far less ergonomic even with these helper methods and so a MiniYamlBuilder is introduced to retain mutable creation functionality. This allows those areas to continue to use the old mutable structures. The main users are the update rules and linting capabilities.
- Enforce SA1604 ElementDocumentationShouldHaveSummary.
- Enforce SA1629 DocumentationTextShouldEndWithAPeriod.
- Turn off some rules covered by IDExxxx rules.
- Remaining rules are treated as part of OpenRA style.
