Optimize AI: Reduce CPU Usage With Tick Rates

Hey guys! Ever notice your game chugging when there are tons of enemies on screen? You're not alone! A common culprit is the CPU usage spiking because of all the calculations happening every frame. Especially when you've got a significant amount of enemies, those _physics_process calls can really add up. So, how do we fix this and make our game run smoother? Let's dive into how we can optimize our AI calculations by using a defined tick rate.

The Problem: Too Many Calculations Every Frame

Think about it: each enemy is constantly calculating its next move, checking for the player, updating its position, and a bunch of other stuff, every single frame. This is especially taxing on the CPU when you have a large number of enemies. We're talking about hundreds, maybe even thousands, of calculations happening multiple times per second. It's like trying to do all your homework the night before it's due – overwhelming!

When we have a huge number of enemies, performing these calculations inside the _physics_process function can lead to massive CPU spikes. The _physics_process function is called every physics frame, which is usually 60 times per second. For each enemy, the game checks if the player is alive, finds the player, and updates the AI movement target. All these frequent checks and updates rapidly increase CPU usage, potentially leading to performance issues such as lag or frame rate drops. It’s like trying to juggle too many balls at once; eventually, something’s going to drop.

For instance, if you have 100 enemies, each performing these calculations 60 times a second, that's 6000 calculations per second just for AI movement! No wonder our CPUs are sweating! The key is to find a way to reduce the frequency of these calculations without making the AI seem unresponsive or dumb. We need to balance performance with gameplay quality, ensuring our enemies are still challenging and engaging but not bringing our game to its knees.

The goal is to distribute the workload more evenly over time. Instead of doing everything at once, we’ll spread out the calculations, giving the CPU a chance to breathe. This is where a defined tick rate comes in handy. By moving some of these calculations to a timer, we can control how often they happen, reducing the load on the _physics_process and making our game run much smoother. Think of it like delegating tasks in a team – everyone gets a fair share, and things run more efficiently.

The Solution: Implementing a Defined Tick Rate for AI Updates

Okay, so how do we actually do this? The idea is to move some of the less critical AI calculations out of the _physics_process function and into a timer-based system. This way, we can control how often these calculations happen, reducing the load on the CPU. It’s like switching from a constant sprint to a more sustainable jog.

Setting Up the AI Update Timer

First, we'll create a Timer node. This timer will trigger a function at a set interval, allowing us to update the AI at a defined rate. Here’s the code:

# Set up AI update timer
ai_update_timer = Timer.new()
ai_update_timer.name = "AIUpdateTimer"
ai_update_timer.wait_time = 0.1  # Update AI position 10 times per second
ai_update_timer.timeout.connect(_on_ai_update_timer_timeout)
ai_update_timer.autostart = true
add_child(ai_update_timer)

Let's break this down:

• ai_update_timer = Timer.new(): We create a new Timer instance.
• ai_update_timer.name = "AIUpdateTimer": We give our timer a name for easy identification in the editor.
• ai_update_timer.wait_time = 0.1: This is the crucial part. We set the wait_time to 0.1 seconds. This means the timer will trigger every 0.1 seconds, or 10 times per second. This is our defined tick rate – we've chosen to update the AI 10 times per second instead of 60, which is a significant reduction in calculations.
• ai_update_timer.timeout.connect(_on_ai_update_timer_timeout): We connect the timer's timeout signal to a new function called _on_ai_update_timer_timeout. This function will be executed every time the timer reaches its wait_time.
• ai_update_timer.autostart = true: We set autostart to true so the timer starts automatically when the node is added to the scene.
• add_child(ai_update_timer): Finally, we add the timer as a child of the current node.

Moving Calculations to the Timer's Timeout Function

Now that we have our timer, we need to move some of the AI calculations from the _physics_process function to the _on_ai_update_timer_timeout function. We identified two key areas for optimization:

1. Targeting the player: Finding and targeting the player.
2. Updating AI movement: Updating the AI's target position.

Here's the code snippet showing the calculations we moved:

# Always target player if alive
if not target_player or not is_instance_valid(target_player):
	_find_player()

# Update AI movement target if we have a player
if target_player and not is_fleeing:
	var ai_controller = get_node_or_null("AIMovementController")
	if ai_controller and ai_controller.has_method("set_target_position"):
		_update_ai_target_position(ai_controller)

These calculations used to happen every frame in _physics_process. Now, they will only happen 10 times per second, thanks to our timer. This dramatically reduces the CPU load, especially when dealing with a large number of enemies.

Implementing the Timeout Function

Next, we need to create the _on_ai_update_timer_timeout function where we'll perform these calculations. Here’s the code:

# Timer callback for AI target position updates
func _on_ai_update_timer_timeout():
	# Check if AI is disabled
	if DebugSettings.instance and not DebugSettings.instance.mob_ai_enabled:
		return

	# Always target player if alive
	if not target_player or not is_instance_valid(target_player):
		_find_player()	
	
	# Update AI movement target if we have a player
	if target_player and not is_fleeing:
		var ai_controller = get_node_or_null("AIMovementController")
		if ai_controller and ai_controller.has_method("set_target_position"):
			_update_ai_target_position(ai_controller)

Let’s break this down as well:

• func _on_ai_update_timer_timeout():: This defines our timeout function, which is called every time the timer reaches its wait_time.
• if DebugSettings.instance and not DebugSettings.instance.mob_ai_enabled: return: This is a handy check to see if AI is disabled in our debug settings. If it is, we simply return, skipping the AI calculations. This is great for testing and debugging.
• The rest of the function is the same code we moved from the _physics_process function. We check if we need to find a new player target, and if so, we call the _find_player() function. Then, if we have a target player and the AI isn't fleeing, we update the AI's target position using _update_ai_target_position(). These functions are critical for the AI's behavior, ensuring enemies can track and engage with the player effectively.

Why This Works: Balancing Performance and Gameplay

So, why does moving these calculations to a timer help so much? It all comes down to reducing the frequency of CPU-intensive tasks. By updating the AI's target position only 10 times per second instead of 60, we've effectively reduced the CPU load by a factor of six for these calculations. That's a huge win!

This approach balances performance with gameplay. While updating the AI 10 times per second might sound like a big drop from 60, in practice, it's often imperceptible to the player. The enemies still react quickly and intelligently, but the CPU isn't being hammered as hard. It’s all about finding that sweet spot where performance is optimized without sacrificing the quality of the gameplay experience.

Imagine the difference: before, every enemy was constantly re-evaluating its situation 60 times a second. Now, they take a breath, think about it 10 times a second, and then act. This small change can make a huge difference in overall game performance, especially when you have a horde of enemies bearing down on the player.

Further Optimizations and Considerations

While this approach is a great start, there are other things you can do to further optimize CPU usage with AI. Here are a few ideas:

• Distance-based updates: You could update AI more frequently for enemies that are close to the player and less frequently for those that are far away. This is because enemies closer to the player are more likely to be actively engaging in combat, while distant enemies might not even be on the screen.
• Culling: Implement culling techniques to disable AI updates for enemies that are off-screen. If an enemy isn't visible, there's no need to update its AI, saving valuable CPU cycles.
• Behavior Trees: Consider using behavior trees for more complex AI logic. Behavior trees can help you structure your AI code in a more organized and efficient way, making it easier to optimize.
• Profiling: Use Godot's built-in profiler to identify other performance bottlenecks in your game. The profiler can help you pinpoint areas where CPU usage is high, allowing you to focus your optimization efforts effectively.

Implementing these techniques can help you create a game that runs smoothly even with a large number of enemies. It's all about being smart about how you use your CPU resources and finding the right balance between performance and gameplay.

By implementing a defined tick rate for AI updates, you can significantly reduce CPU usage, especially when dealing with a significant amount of enemies. This simple change can make a big difference in your game's performance, allowing you to create more complex and engaging gameplay without sacrificing frame rates. Remember, optimization is key to a smooth and enjoyable gaming experience!