fastcatan

A high-throughput Settlers of Catan simulator — C++ core with Python bindings (nanobind), built for reinforcement-learning research. Millions of environment steps per second, a flat observation vector, a legal-action bitmask, and a batched vectorized environment for GPU training.

• ⚡ Fast — pure-C++ rules engine, ~4.6M batched env-steps/s on a single desktop.
• 🧠 RL-ready — fixed-size float32 observation, uint64 action mask, scalar reward.
• 📦 Batched — BatchedEnv steps thousands of games per call (OpenMP, GIL released).
• 🎯 Deterministic — seeded games are fully reproducible.

Stability: the rules engine and the observation/action interface are frozen and stable — pin an exact version (e.g. fastcatan==1.0.2).

Install

pip install fastcatan

Prebuilt wheels: Linux x86_64 and macOS (Apple Silicon), CPython 3.12+ (one abi3 wheel serves 3.12 / 3.13 / 3.14). Other platforms build from the sdist automatically (needs a C++ toolchain + CMake ≥ 3.27).

Quickstart

Play one game with a random legal policy:

import numpy as np
import fastcatan

env = fastcatan.Env()
env.reset(seed=0)

mask = np.zeros(fastcatan.MASK_WORDS, dtype=np.uint64)   # legal-action bitmask
obs  = np.zeros(fastcatan.OBS_SIZE,  dtype=np.float32)   # observation buffer
rng  = np.random.default_rng(0)

while True:
    env.action_mask(mask)                 # fill legal moves for current player
    env.write_obs(env.current_player, obs)   # fill that player's POV observation
    legal = np.flatnonzero(np.unpackbits(mask.view(np.uint8), bitorder="little")
                           [:fastcatan.NUM_ACTIONS])
    action = int(rng.choice(legal))
    reward, done = env.step(action)
    if done:
        vps = [env.player_vp(p) for p in range(fastcatan.NUM_PLAYERS)]
        winner = next((p for p, v in enumerate(vps) if v >= 10), -1)
        print("winner:", winner, "vps:", vps)
        break

Batched (vectorized) environment

For RL throughput, step many games at once and drive them with a batched policy:

import numpy as np, fastcatan

N = 4096
env = fastcatan.BatchedEnv(N, seed=0)
env.reset()

masks = np.zeros((N, fastcatan.MASK_WORDS), dtype=np.uint64)
obs   = np.zeros((N, fastcatan.OBS_SIZE),  dtype=np.float32)
acts  = np.zeros(N, dtype=np.uint32)
rew   = np.zeros(N, dtype=np.float32)
done  = np.zeros(N, dtype=np.uint8)

env.write_masks(masks)      # all N legal masks in one C++ call
env.write_obs(obs)          # all N current-player observations
# ... your batched policy fills `acts` ...
env.step_raw(acts, rew, done)   # steps all N games (OpenMP, GIL released)

API at a glance

env.step(action) returns (reward, done). Observations are written into caller-provided buffers (no per-step allocation). See examples/ for random and alpha-beta players.

Development

Build from source (tunes -march=native for your machine):

pip install .
pytest tests/

License

MIT © 2026 s1nbo