Home GitHub RSS

Commanding RTS Commands

Scaling State Mutations via FSM Visitors

DownFlux is a real-time strategy game in active development at github.com/downflux. The goal of this project is simply to learn and have fun. I have several years of professional software development experience, none of which is in the game industry. This document does not advocate a general form solution for all state mutation problems, but rather demonstrates a different view of the command pattern. For a more technical and detailed overview of this approach, take a look at the design doc.

I mix first person plural in this document liberally because it sounds awkward to keep saying “I” all the time, not because I’m royalty.


A major problem we’re facing while working on DownFlux has been finding a scalable approach to state mutations. Scalability here represents the ability for us to remain agile when implementing new mutation flows – this encompasses general good software development guidelines like testability, code “fragrance” (i.e. lack of smell), and framework flexibility.

Our model of a mutation flow consists of a command scheduler object, housing a metadata object per distinct flow invocation. These metadata objects are a thin wrapper around a finite state machine (FSM), and exposes a minimal subset of the game state to a visitor object.

Our metadata objects may only call read-only queries to the game state, and returns a calculated state to the visitor. The visitor may invoke write operations on both the metadata and the underlying state.

See a snapshot of our repo for more details. Feel free to reach out on Reddit or Twitter with questions or comments.


  • state mutations, flows, commands: a series of changes to the game state (e.g. map, entities, etc.) which achieve a specific end-goal (e.g. move)

Flow Examples

  • move(source, dest): move the source object to the destination location.
  • chase(source, target): series of serialized moves, which are updated as the destination object moves.
  • attack(source, target): chase target asynchronously; if target is within attack range and the source can attack (off cooldown), then commit state change.

An ad hoc Approach

The first attempt we made at implementing a state mutation “framework” skipped any consideration of scalability or maintainability for the sake of an MVP. Here is our single move command:

func (s *Server) doTick() {
  for {
    for c := range s.Commands() {
      // Client calls mutate this CommandQueue object by appending
      // pending commands.

type Command interface {
  Execute(args interface{}) error
  Type() CommandType

func (c *MoveCommand) Execute(args interface{}) error {
  a = args.(MoveCommandArg)

  // p is a list of Position objects (i.e. (x, y) tuples).
  p = c.map.GetPath(a.Source.Location.Get(a.Tick), a.Destination)

  // Source merges the positions with internal velocity in
  // the curve.
  return nil

Figure 1: Simple implementation of the move command.

Yup. This moves things. How do we start overengineer this?

Our second order approximation takes into consideration GetPath is expensive – we’re making a full A* search. But in an RTS game, it is very often the case that the player direct units to a different location before the unit reaches the target, wasting a lot of compute cycles.1 Therefore, we want to calculate and set a partial trajectory instead, with delayed execution of the rest of the path.2

Partial Move DAG

Figure 2: Partial path diagram. The command should only calculate p0 first; at some time t in the future, recalculate the path (which may involve further sub-path iterations).

With the partial path logic, our command now looks something like this:3

func (s *Server) doTick() {
  for var args := range s.q {
    c.Execute(curTick, args)

// Called by client API as well as internally.
func (c *MoveCommand) Schedule(
  t Tick,
  e Entity,
  d Destination) error {

  scheduledAction := c.q.Get(e)
  if scheduledAction != nil && scheduledAction.Precedence(t) {
    c.q.Set(t, e, d)

func (c *MoveCommand) Execute(t Tick, args interface{}) {
  const pathLen int = 10;
  var arg := args.(MoveCommandArg)

  // Return a path of a specific length instead.
  p = c.Map.GetPath(


  // Schedule partial path execution if the last element of the path is not
  // the "true" destination. c.Schedule() also needs to calculate if there are
  // any existing commands that need to be overwritten.
  if p[len(p) - 1] != arg.Destination {
      t + a.Source.CalculateTravelTime(p),
  } else {

Figure 3: Toy move command implementation v2 – here we enqueue a delayed move command into the main queue. This queue may have client- or other server-initiated command scheduling, so when we update the queue, we need to ensure there is a single, canonical execution flow; this logic is packed into the Schedule() function, meaning a single command will need to know the implementation logic / hierarchy of all other commands.

Kind of a pain, but still doable.

This model worked well enough for us to get a rudimentary frontend client running; however, a gut check seems to indicate major scalability issues with this approach.4 In particular,

  1. A command may act on multiple entity types, and an entity may have multiple mutation flows – the implementations so far already demonstrates this vulnerability IMO.
  2. Because the command queue contains commands from all command implementations (i.e. move, attack, etc.) and the command may mutate the queue (e.g. partial move enqueues), the command must know the details of all siblings flows.
  3. The command must manually check the global state each time it is invoked, e.g. if the source has reached the destination. It is unclear how each command will implement this state read, which will impede maintainability.
  4. Command.Execute() read and writes to the global state; from our simple move example, this already seems like a testability nightmare and needs to be addressed.

A common theme to these issues is the broad scope and authority we have conferred upon the command object; how can we clamp down on this?

(An Accidental) Tour de Entities

The first concern seems like a classic double dispatcher problem between the command and the entities (e.g. tanks) that they mutate. This seems to suggest we should break out the command into a visitor pattern implementation.

func (s *Server) doTick() {
  for var v := range s.Commands() {
    for var e := range s.Entities {

func (e *EntityImpl) Accept(v Visitor) { v.Visit(e) }

func (c *MoveCommand) Visit(e Entity) {
    if !e.IsMoveable() { return }

    if c.q.Has(e) {
      // This is the same implementation as in [Figure 3](#figure-3).
      c.Execute(c.Status.CurrentTick(), ...)

Figure 4: The architectural change counterpart to the changes made in Figure 3.

There are some flaws here.

  1. The Acceptor object is a single game entity – this is not abstract enough. Consider the attack command which mutates both the attacker and target – how do we visit target in an AttackCommand? Do we need a DealDamageVisitor? If so, suggests we will need a message broker between attacking and taking damage, which seems unnecessarily overwrought.
  2. The command still has to deal with the schedule (c.q), which is a global mutatable state. As mentioned in Figure 2, the schedule may be edited by both sides of the network divide, and having our command dealing with that logic directly seems messy.

Note that this refactor was actually useless in terms of reducing tech debt, but was very important in exposing the points of friction that we will need to address.

Finite State Metadata

Let’s examine the first concern above, where we’re dealing with pain points brought up by iterating over the entities themselves in a command. Because we’re visiting the entity, that means any broader details about the execution (including e.g. partial move cached data) still need to be managed by the command object:

type MoveCommand struct {
  // Reference to global state.
  q []MoveCommandArg

func (c *MoveCommand) Visit(e Entity) {
  if c.q.Has(e, ...) { ... }  // See [Figure 4](#figure-4)

This seems inefficient – why are we accepting a non-scheduled entity as valid input? In fact, our first approach was probably closer to the mark – let’s just pass the command metadata as input instead!

func (c *MoveCommand) Visit(m MoveCommandArg) { ... }

One key difference between this and our initial implementation is how we’re approaching the metadata object here – we’re promoting the metadata into a “real” data struct, and as such, we need to consider the exported metadata API. What does a command need from the metadata?

In the case of move (with partial implementation), we need to track when the next iteration of partial paths need to be calculated. Seems like a job for an FSM!

type CommandMetadata interface {
  Status() FSMState
  Transitions() map[FSMState]FSMState

  // Used to determine which command needs to be canceled.
  Precedence(o CommandMetadata) bool

  // Triggered by Schedule or a Command.

// MoveCommandArg will implement the CommandMetadata interface.
type MoveCommandArg struct {
  scheduledTick Tick
  source        Moveable
  destination   Position

Figure 5: Expanded MoveCommandArg type from Figure 1.

Where the FSM DAG for MoveCommandArg is as follow:

Move DAG

Figure 6: move state diagram.

The most straightforward way to link this into MoveCommand.Visit() looks something like this:

func (c *MoveCommand) Visit(m *MoveCommandArg) {
  if m.Tick() == curTick {
  if m.Status() == EXECUTING {
    p = c.Map.GetPath(..., pathLen)

    // Need to schedule next iteration.
    if m.Destination() != p[len(p) - 1] {
  if m.Source().Location(curTick) == m.Destination() {

Figure 7: move implementation with partial paths and FSM metadata inputs.

This seems cleaner than what we had before! We have a formal FSM structure validating the partial command action being executed. Additionally, because we’re passing a reference to the metadata object into Visit(), we can migrate the schedule away from the command.

This still seems a bit messy though, when we have to call SetStatusOrDie so many times. Is there a way we can not do that?


Read-Only FSMs

We observe that the state of an FSM is an explicit representation of the underlying system. It does not matter how we calculate this state! In Figure 7, we “calculated” the state by storing it as an internal variable via SetStatusOrDie(), but we can also treat the state as a generic read-only operation on the system.

As an example, let’s consider the state diagram of the move command:5

  • FINISHED: A move command is finished if the source entity has arrived at the given destination.
  • PENDING: If the internal m.scheduledTick does not equal the current tick, the command is not yet ready to execute; this accounts for both when the source is already moving, or still needs to calculate the next partial move.
  • EXECUTING: If m.ScheduledTick equals current game tick, the command needs to take action and actually calculate the path of the object. At the end of the execution phase, the scheduled tick should be updated.
  • CANCELED:6 An externally triggered transition if e.g. the client specifies another move command in the meantime. This may need to be explicitly set.

So in code form, this looks something like this:

// MoveCommandArg will implement the CommandMetadata interface.
type MoveCommandArg struct {
  scheduledTick Tick
  isCanceled    bool

  // References the actual game state.
  status        *TickStatus  // Exports CurrentTick().
  source        Moveable
  destination   Position

func (m *MoveCommandArg) Status() FSMStatus {
  if m.isCanceled == CANCELED { return CANCELED }
  if m.source.Location.Get(m.status.CurrentTick()) == m.destination {
    return FINISHED
  if m.scheduledTick == status.CurrentTick() {
    return EXECUTING
  return PENDING

func (c *MoveCommand) Visit(m MoveCommandArg) {
  if m.Status() == EXECUTING {

Figure 8: Toy implementation of the move command with smart metadata objects.

By making the metadata a bit smarter, we’ve greatly reduced the burden on the execution logic. Note that the metadata object itself is read-only – we are ensuring that only the command object has the ability to write to the game state, as well as to the metadata object (e.g. SetScheduledTick()). Our server tick logic currently looks like this:

func (s *Server) doTick() {
  for var v := range s.Visitors() {
    for var q := range s.q[v.Type()] {
      // It is up to each metadata list to decide if it may be run in parallel
      // or not.

To pause a second, here is what our infrastructure looks like at the moment:

FSM Visitor DAG

Figure 9: FSM / Visitor relationship diagram. The dirty state component is outside the scope of this blog post, but is explained in the design doc.

Two-Pass Scheduler

The other friction point we had was with regards to the complexity of having the command pushing into a two-way schedule (i.e., one that is directly mutated by both the client and server). We need a way to control the timing of when schedule mutations are made.

Our solution to this problem was to implement a client-only schedule object which is used as a scratchpad for incoming requests. At the beginning of each tick, we merge this into our actual source-of-truth schedule:

type Schedule interface {
  Append(t VisitorType, m CommandMetadata)

  // Requires CommandMetadata to implement Precedence().
  Merge(o Schedule)

func (s *Server) doTick() {

  for var v := range s.Visitors() { ... }

Figure 10: Two-pass schedule implementation.

This ensures when commands are running, the command has exclusive write access to the schedule – since only instances of the same command are executing at the same time, reasoning about concurrency becomes greatly simplified.


An interesting tangent: a core application of the visitor pattern is for a double-dispatch table; however, note that we have a strict one-to-one relationship between a single CommandMetadata implementation and a command. There is no double dispatch here.

However, the reason why a visitor pattern is good when solving for the double-dispatch is because it forces a decoupling of the underlying data object from the mutations. It is our good fortune that we chose to view the problem through this lens, even if we originally applied the pattern inappropriately.

If we wished, we can migrate back to using a simple for loop to call the commands, as we originally did, but safe in the knowledge that we have arrived at a scalable approach to building state mutation flows.

func (s *Server) doTick() {
  for var c := range s.Commands() {
    for var m := range s.q[c.GetType()] {
      // If the command wants to run serially, it may employ a class-level lock
      // on Execute().
      go c.Execute(m)
    // Wait for all invocations to return before continuing to next command.

Chaining Commands

Let’s apply the same pattern to the attack command, a flow which has a dependent chase action.

type AttackMetadata struct {
  s     CanAttack
  t     CanDie  // Mortal?
  chase *ChaseMetadata

func (m *AttackMetadata) Status() Status {
  if chase.Status() == CANCELED { return CANCELED }
  if t.Health(curTick) <= 0 {
    return FINISHED  // Cleaned up next tick.
  if d(s, t) < s.AttackRange() && a.OffCooldown(curTick) {
    return EXECUTING
  return PENDING

func (c *AttackCommand) Visit(m AttackMetadata) {
  if m.Status() == EXECUTING {

Figure 11: Simplified attack command implementation.7

Dependencies in our framework are modeled by a pointer in the metadata to another metadata object; the encompassing flow can then incorporate the dependent flow status when reporting its own status. We have yet to encounter a case where the command needs to query a dependent step’s status direcly.

A command may need to enqueue a dependent flow. For example, consider an entity commanded to guard an area – when an enemy enters the entity’s line of sight, guard may decide to enqueue an attack. In this case, the guard command will have a reference to the attack schedule and call q.Append().

Canceling Commands

q.Append() and q.Merge() will invoke CommandMetadata.Precedence(), which tests for the relative priority of two metadata objects. The lower priority one will be canceled.

CommandMetadata.Cancel() is command-dependent, but should also trigger the Cancel() function of dependencies. An upstream / parent command which need the child finish can then query the child flow status when reporting its own Status().

See Also


Recontextualizing as Event Flows

I came across a rather interesting tech talk while writing this article which talks about the Event-carried State Transfer software pattern (indeed, from what little research I’ve done on this, it seems like this talk is actually the talk which introduced the concept to the wider public).

There are some interesting parallels here between the event-driven approach described and ours here. Indeed, the state query in the command executor is just detecting if an event occurred between the last and current server tick. Moreover, the event-carried state transfer pattern seems to emphasize minimizing data access to the underlying state. The event pattern achieves this through some level of caching, packed into the event data in order to reduce resource contention. Our implementation instead minimizes the API surface area that is exposed through the command metadata.

It is true that we could massage our current approach into an event-driven approach; however, this seems both overengineered and antithetical to how we view our code.

  1. Remember that we are treating the game system as deterministic. When an object moves, the partial move schedule is already preordained – there is no additional user input that is necessary in order to make the system behave correctly. Our framework accounts for this by doing a series of state reads. However, if we were to transform the state transitions into broadcasted events, we’re asserting instead the system is always in flux, and we’re “promoting” deterministic behavior into the category of “unexpected” inputs. This seems like a less elegant approach, and at the same time will require a large system overhaul for questionable value (for our use-case).
  2. A single server tick will execute a list of commands in a known order, e.g. we process all move commands, then all attack commands, etc. Event queues are very useful when we are decoupling execution order from our server; however, if we were to do this, then a whole new, scary world of consistency problems appear. We can leave that problem to concurrent text editors and CRDTs.

A Digression on Attack Variants

While editing this document, a friend pointed out the toy implementation of the attack command does not fully specify some edge-case behavior –

I know you said this is simplified, but how do you handle situations where the command calls for a stationary source (e.g. tesla coil) to attack a target which then leaves its range?

Does it stay in the command queue in case the target comes back into range, with some lower-priority “auto attack” command dealing damage to nearby enemies in the meantime? Or does it cancel itself?

This question demonstrates a nice property of the FSM / visitor approach, which is the flexibility of implementation. The implementation in Figure 11 assumes that the target can move, and will always try to attack the same target until the target dies. How do we extend this command?

We can envision an attack variant that forgets the target after the target goes out of range:

type ForgetfulAttackMetadata struct {
  s           CanAttack
  t           CanDie
  hasAttacked bool

func (m *ForgetfulAttackMetadata) Status() Status {
  if t.Health(curTick) <= 0 {
    return FINISHED  // Cleaned up next tick.
  if d(s, t) < s.AttackRange() && a.OffCooldown(curTick) {
    return EXECUTING
  if m.hasAttacked && d(s, t) >= s.AttackRange() {
    return CANCELED  // Cleaned up next tick.

  return PENDING

func (c *ForgetfulAttackCommand) Visit(m ForgetfulAttackMetadata) {
  if m.Status() == EXECUTING {

Figure 12: Alternative attack command implementation. Which cancels itself if the target exits range via a read-only operation.

Partial Tick Execution

Because the metadata is stored in a separate queue, we can pause command execution at any given time during a tick – this means we can smooth out large server loads over several ticks, allowing us to enforce a consistent server tick rate (at the expense of some additional end-to-end latency). This feature is not currently implemented, but may be of use later.


  1. Partial pathfinding is implemented via hierarchical A*, though this may / will change in the future. The point is that there may be additional complexity introduced into commands. As an interesting sidenote, partial pathfinding allows us to spread out pathfinding to multiple workers after the initial coarse-grain search. This may be a nice optimization route to go down in the future. 

  2. Future implementations of pathfinding, e.g. via flow fields or navmesh-based solutions, may eliminate the need for partial paths. 

  3. In reality, this step was implemented along with initial visitor pattern migration (explained later), but we’re highlighting a rather important motivating point for seeking better approaches to the problem. 

  4. While interface{} inputs are undesirable, they aren’t necessarily an architectural problem. We’re concerned with what are potential project-terminators due to non-maintainability. 

  5. For more information on this, see Time-Invariant Finite State Machines. State transitions are traditionally triggered by an “external” user; we are expanding the FSM here to allow for the possibility that transitions may be triggered without an explicit outside trigger action. This allowance gives us a lot of flexibility in modeling semi-autonomous commands. 

  6. Sidenote, I learned the objectively better “cancelled” spelling is British, and so have reverted to the inferior but semantically consistent American spelling. 

  7. For a more in-depth discussion of the attack command implementation details, see A Digression on Attack Variants 

Comment on Reddit.
Minke Zhang
Minke is a software engineer based in the US. He enjoys running, climbing, photography, and banana-related facts. He works on DownFlux in his spare time. Minke prefers spaces over tabs in Python.