Parallelising Robot Simulations¶

Evaluating a population of robots is the main bottleneck in evolutionary robotics — every individual needs to be simulated before selection can proceed, and simulations are fully independent of each other. ARIEL supports two complementary approaches to parallelising this step.

Choosing an approach¶

	MuJoCo Worker	Ray
Best for	Single-machine physics simulation	Multi-machine clusters or general Python tasks
Parallelism mechanism	`multiprocessing.Pool` with `spawn` context	Distributed actor/task system
Dependencies	Python standard library only	Requires `ray`
MuJoCo isolation	✅ Spawn context avoids OpenGL/thread conflicts	⚠️ Requires manual care around MuJoCo state
Cluster deployment	❌ One machine	✅ Multi-machine and cloud
Per-task overhead	Low — direct OS process fork	Higher — Ray object store serialisation
Setup complexity	Subclass one class, pass to `pool.imap`	`ray.init()` + `@ray.remote` decorator
Debugging	Standard Python tracebacks	Ray dashboard and distributed logs

If you are running experiments on a single workstation, use the MuJoCo Worker approach — it has less overhead, plays nicely with MuJoCo’s internal threading, and requires no additional services.

If you need to scale beyond one machine, or want to distribute heterogeneous tasks (simulation, neural-network training, data processing) across a cluster, use Ray.

Pages¶