murk 0.2.0

Python bindings for the Murk simulation framework

murk (Python)

Python bindings for the Murk simulation engine.

murk gives you:

• Native Rust performance via PyO3 bindings
• Gymnasium-compatible environment adapters
• Batched stepping for high-throughput RL training
• Typed Python API surface (py.typed + .pyi stubs)

Install

python -m pip install murk

Requirements:

• Python 3.12+

Quick Start (Gymnasium)

MurkEnv is designed for subclassing. Override hook methods to map actions, reward, and episode boundaries for your task.

import numpy as np
from murk import (
    Config,
    EdgeBehavior,
    FieldMutability,
    MurkEnv,
    ObsEntry,
    PropagatorDef,
    WriteMode,
)


class SimpleEnv(MurkEnv):
    def __init__(self, seed: int = 42):
        cfg = Config()
        cfg.set_space_line1d(10, EdgeBehavior.Absorb)
        cfg.add_field("value", mutability=FieldMutability.PerTick)
        cfg.set_dt(0.1)
        cfg.set_seed(seed)

        def step_fn(reads, reads_prev, writes, tick_id, dt, cell_count):
            writes[0][:] = float(tick_id)

        PropagatorDef("inc", step_fn, writes=[(0, WriteMode.Full)]).register(cfg)
        super().__init__(cfg, [ObsEntry(0)], n_actions=2, seed=seed)
        self._tick_limit = 100

    def _compute_reward(self, obs, info):
        return -float(np.sum(obs))

    def _check_terminated(self, obs, info):
        return False

    def _check_truncated(self, obs, info):
        return info["tick_id"] >= self._tick_limit


env = SimpleEnv()
obs, info = env.reset(seed=0)
obs, reward, terminated, truncated, info = env.step(0)
env.close()

High-Throughput Vectorized RL

BatchedVecEnv steps all worlds in one Rust call (single GIL release), which removes per-world FFI overhead.

import numpy as np
from murk import BatchedVecEnv, Config, EdgeBehavior, FieldMutability, ObsEntry


def make_config(i: int) -> Config:
    cfg = Config()
    cfg.set_space_line1d(64, EdgeBehavior.Absorb)
    cfg.add_field("energy", mutability=FieldMutability.PerTick)
    cfg.set_seed(i)
    return cfg


env = BatchedVecEnv(
    config_factory=make_config,
    obs_entries=[ObsEntry(0)],
    num_envs=32,
)
obs, infos = env.reset(seed=0)
obs, rewards, terminateds, truncateds, infos = env.step(np.zeros(32))
env.close()

Low-Level API

If you want direct control of stepping and commands, use World and ObsPlan from murk._murk / murk.

Core types:

• Config, World, Command
• ObsEntry, ObsPlan
• StepMetrics (timings, queue/realtime counters, sparse reuse counters)

Package Links

• Repository: https://github.com/tachyon-beep/murk
• Docs: https://tachyon-beep.github.io/murk/
• Issues: https://github.com/tachyon-beep/murk/issues