Glossary
The vocabulary of RMFS simulation, discrete-event modeling, and the WareMax RL benchmark.
- RMFS
- Robotic Mobile Fulfillment System — a warehouse where autonomous mobile robots carry storage pods to stationary human pickers, in the style of Kiva / Amazon Robotics. WareMax simulates the pod-to-person dispatching layer of an RMFS.
- AMR
- Autonomous Mobile Robot. In WareMax, robots are AMRs moving on a graph topology with a configurable maximum speed; the simulator does not model AS/RS cranes or AGV tuggers.
- Pod-to-person
- A fulfillment pattern where robots bring inventory pods to a fixed pick station, rather than a picker walking to the inventory. WareMax models pod-to-person only.
- DES
- Discrete-Event Simulation — a modeling approach where the simulation clock advances only at discrete events, never continuously. WareMax uses a single-threaded event-queue DES kernel.
- Dispatching policy
- The rule that decides which robot handles which pick task (task allocation). WareMax ships five heuristic baselines and supports learned policies via the RL interface.
- Gymnasium
- The maintained successor to OpenAI Gym — a standard API for reinforcement-learning environments. WareMax exposes WaremaxAllocEnv as a Gymnasium environment via PyO3.
- Reward mode
- How the RL environment scores a decision. WareMax provides four: sparse, dense, attribution, and routed. The routed mode charges only the controllable cost.
- Causal delay attribution
- A decomposition of each completed task’s cycle time into five buckets — assignment, travel, queue, congestion, service — that sum to the whole. Used for analysis and as an RL reward signal.
- ChaCha8 seed
- The random number generator (ChaCha8) seeded from a u64 that drives all randomness in the simulator. Fixing the seed is what makes a run reproducible.
- Canonical tie-breaking
- Breaking ties by entity id rather than by iteration order, applied everywhere in WareMax so that two runs with the same seed produce byte-identical trajectories.
- Congestion-aware routing
- Routing that accounts for node and edge capacities and a configurable congestion weight, so robots contend for shared graph resources realistically (waremax-map).
- SMDP
- Semi-Markov Decision Process — the framing WareMax uses for the RL problem, where the agent acts only at decision points (a robot becomes free and a task waits), not on a fixed timestep.
- Byte-identical trajectory
- The property that the exact sequence of simulator states and events is bit-for-bit reproducible given the same seed and action sequence. WareMax enforces this with tests.