Optimize collision detection performance for AI training in pygame

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1

I’m working on a machine learning project where I need to teach an AI agent to navigate around obstacles using pygame. The setup is similar to the classic Snake game where the AI needs to avoid walls and player trails.

My current approach uses multiple sensors that extend from each player to detect nearby obstacles. Each sensor is built from several small rectangular sprites that check for collisions with obstacle sprites. When a collision happens, I calculate the distance and pass this data to the neural network.

However, the collision detection is extremely slow and takes up around half of my program’s execution time according to profiling results.

Here’s my current sensor implementation:

def generate_sensor_rays(player_x, player_y, heading, ray_spacing, field_of_view, ray_length):
    directions = [angle for angle in np.ceil(np.arange(heading-field_of_view, heading+field_of_view, ray_spacing))]
    
    move_x = np.cos(np.deg2rad(heading))
    move_y = np.sin(np.deg2rad(heading))
    
    return list(map(functools.partial(build_ray_segments, player_x, player_y, ray_length), directions))

def make_segment(player_x, player_y, delta_x, delta_y, step):
    return create_sprite(player_x + step * delta_x, player_y + step * delta_y, 1, 1, 0, RED)

build_segment = make_segment

def build_ray_segments(player_x, player_y, max_length, direction):
    segments = pygame.sprite.Group()
    add_segment = segments.add
    
    delta_x = np.cos(np.deg2rad(direction))
    delta_y = np.sin(np.deg2rad(direction))
    steps = [step for step in range(2, max_length, 8)]
    add_segment(map(functools.partial(build_segment, player_x, player_y, delta_x, delta_y), steps))
    return segments

build_ray = build_ray_segments

My obstacle class:

class GameObstacle(pygame.sprite.Sprite):
    def __init__(self, pos_x, pos_y, w, h, rotation, sprite_color):
        super().__init__()
        self.image = pygame.Surface([w, h])
        self.image.fill(sprite_color)
        self.rect = self.image.get_rect()
        
        self.rect.centerx = pos_x
        self.rect.centery = pos_y

I’ve tried several optimizations like using map functions instead of loops and caching function references, but nothing helped much. I also experimented with Bresenham’s algorithm for line drawing, which was faster but missed obstacles because it only checked exact pixel coordinates rather than overlapping areas.

What are some better approaches for fast obstacle detection in pygame? Any suggestions for improving this collision system would be really helpful.

2

Skip the hundreds of sprite objects - you’ll kill your performance. Switch to math-based collision detection with spatial partitioning instead. I hit the same wall building pathfinding AI for a tower defense game. Here’s the thing: pygame’s sprite system is meant for visuals, not heavy computational geometry. Break your game world into a grid and only check collisions in relevant cells. For each sensor ray, just use basic line-rectangle intersection math instead of sprite collisions. What really saved me was precalculating distance fields. If your obstacles don’t change much, precompute distance values for the whole area and lookup sensor readings instead of real-time collision checks. Dropped my collision overhead from 60% to under 5% of total execution time. Also try reducing sensor resolution during training - save the high-precision stuff for final evaluation. Most neural networks learn fine with approximate distance readings anyway.

3

Move your collision detection outside pygame entirely - that’s what solved this for me when building AI training sims.

Your setup’s fighting pygame’s design. Sprite groups weren’t built for ML training workloads. Don’t optimize pygame collision detection - move the sensor logic to Latenode workflows instead.

What worked: Let Latenode handle collision math with pure geometry functions. It processes thousands of sensor rays in parallel. Your pygame loop just sends positions and obstacle data to Latenode, gets back sensor readings, and feeds them to your neural network.

Performance boost is huge since Latenode handles the load with proper parallel processing. You can easily swap collision algorithms, adjust sensors, or distribute training across multiple game instances without touching main code.

My training times dropped 80%. Pygame stays clean and fast while Latenode handles the math in background.

Check it out: https://latenode.com

4

Your approach creates way too many temporary objects. I hit the same bottlenecks training RL agents in pygame environments. The problem? You’re generating hundreds of sprite segments every frame - this destroys garbage collection and memory allocation speed.

Ditch the sprite-based ray segments. Cast rays with simple line equations instead. Calculate the parametric line from your agent’s position in each sensor direction, then iterate through collision rectangles using basic line-rectangle intersection tests.

Store obstacles in a spatial hash table or quadtree so you’re only testing nearby objects, not every obstacle in the scene.

I switched from sprite-based sensors to pure mathematical ray casting on my racing AI project. Collision detection time dropped from 45% to 8% of total execution time.

Bottom line: AI training needs fast math, not visual accuracy.