Procedural generation

roguelike

survival-crafting

When to use

• Use to generate maps, dungeons, terrain heightmaps, item drops, or any content you do not want to author by hand.
• Use when results must be reproducible from a seed (debugging, daily challenges, shareable worlds).
• Use to pick weighted random outcomes (loot rarity, spawn tables).

godot-tilemap

unity-tilemap-2d

game-ai

level-design

Core workflow

1. Own your randomness. Create one seeded RNG instance and pass it everywhere. Never call the global/static random in generation code — it makes results irreproducible and order-dependent.
2. Pick the technique for the content. Continuous terrain/heightmaps → noise. Discrete rooms/corridors → space partitioning or agent-based carving. Outcomes with rarities → weighted tables.
3. Generate into a plain data grid/array first, decoupled from rendering. Generation fills

   int[][]

   or a dict; a separate pass draws it.
4. Validate before shipping the result to the player. Is every room reachable? Is the spawn safe? Is there a path to the exit? Reject or repair layouts that fail; do not hand the player a broken map.
5. Tune with the seed fixed so each parameter change is visible in isolation, then sweep seeds to check the distribution, not just one lucky map.

Patterns

1. Seeded, deterministic RNG (the foundation)

import random
rng = random.Random(seed)        # a dedicated instance — NOT the global random.*
room_count = rng.randint(5, 12)  # same seed -> same sequence, every run
# RIGHT: thread `rng` through every function that makes a choice.
# WRONG: calling random.randint(...) (global state) — order-dependent, unseedable.

var rng = RandomNumberGenerator.new(); rng.seed = s

var rng = new System.Random(seed)

UnityEngine.Random.InitState

2. Fractal (fBm) noise for heightmaps

# Sum several octaves: each higher octave has higher frequency, lower amplitude.
def fbm(noise, x, y, octaves=5, lacunarity=2.0, gain=0.5):
    total, amp, freq, norm = 0.0, 1.0, 1.0, 0.0
    for _ in range(octaves):
        total += amp * noise(x * freq, y * freq)   # noise() returns ~0..1
        norm  += amp                                # track total amplitude
        amp   *= gain                               # each octave contributes less
        freq  *= lacunarity                         # ...at a higher frequency
    return total / norm                             # normalize back into 0..1

# Redistribute to carve flat valleys / sharpen peaks: higher exp -> more lowland.
elevation = pow(fbm(noise, nx, ny), 2.2)

FastNoiseLite

opensimplex

Unity.Mathematics.noise

Mathf.PerlinNoise

references/noise.md

3. Weighted loot table (rarity-correct selection)

# Roll proportional to weight: common drops far more often than legendary.
def weighted_pick(rng, table):           # table: list of (item, weight)
    total = sum(w for _, w in table)
    roll = rng.uniform(0, total)          # a point on the cumulative line
    upto = 0.0
    for item, w in table:
        upto += w
        if roll < upto:                   # first bucket the roll falls into
            return item
    return table[-1][0]                   # float-safety fallback

loot = weighted_pick(rng, [("common", 70), ("rare", 25), ("legendary", 5)])

references/dungeon-generation.md

4. Rooms-and-corridors dungeon (sketch)

# 1. Place non-overlapping rooms; 2. connect them; 3. carve into the grid.
rooms = []
for _ in range(attempts):
    r = Rect(rng.randint(1, W-w-1), rng.randint(1, H-h-1), w, h)
    if not any(r.intersects(o.expand(1)) for o in rooms):  # keep a 1-tile gap
        rooms.append(r)
for a, b in zip(rooms, rooms[1:]):       # connect each room to the next
    carve_l_corridor(grid, a.center, b.center, rng)   # horizontal then vertical

references/dungeon-generation.md

Pitfalls

• Using the global RNG inside generation makes worlds unreproducible and breaks the moment call order changes. Always pass a seeded instance.
• Correlated noise fields: sampling elevation and moisture from the same seed/offset produces biomes that line up in bands. Offset or reseed each field.
• Octave artifacts: adding octaves without renormalizing pushes values out of

  0..1

  ; divide by the summed amplitude (and beware library output ranges — some return

  -1..1

  , some

  0..1

  ).
• No connectivity check: rooms or caves can end up isolated. Flood-fill from the spawn and discard/reconnect unreachable regions before play.
• Unbounded placement loops: "keep trying until N rooms fit" can spin forever on a small grid. Cap attempts and accept fewer rooms.
• Seeding once globally, then relying on frame timing: any non-deterministic input (time, physics, hash randomization) leaking into generation destroys reproducibility.

References

• references/noise.md

  — octaves/lacunarity/gain, redistribution, island shaping, two-axis biome lookup, blue-noise object scatter.

• references/dungeon-generation.md

  — BSP, rooms+corridors, random-walk caves, cellular-automata smoothing, connectivity validation, distribution/pity tables.

Related skills

• godot-tilemap

  ,

  unity-tilemap-2d

  — paint the generated grid into the engine.

• game-ai

  — pathfinding over the generated graph.

• level-design

  — pacing and hand-authored structure that procgen complements.

• roguelike

  ,

  survival-crafting

  — genres that compose this skill.