# Configuration

The package itself has no global configuration — every env is configured
per-instance via constructor arguments. The recommended pattern:

1. Choose a benchmark scenario (or your own `.inp`).
2. Pick the env variant — `SwmmRTCEnv`, `SwmmCIPEnv`, `SwmmJointCIPRTCEnv`,
   or `SwmmMORTCEnv`.
3. Build an `ObservationBuilder` describing the feature set.
4. Pick `RewardTerm` instances describing the cost / benefit landscape.
5. For MO envs, pick `ideal_point` and `reference_point` for HV
   normalisation.

## Per-env defaults

The bundled benchmarks (e.g. `b01_twin_tank`) ship with curated
defaults. To customise, instantiate the env directly:

```python
from openswmm_gymnasium.envs import SwmmRTCEnv
from openswmm_gymnasium.observations import ObservationBuilder
from openswmm_gymnasium.rewards import FloodingVolume, PeakOutflow
from openswmm_gymnasium.spaces.runtime import OrificeSetting

env = SwmmRTCEnv(
    "path/to/model.inp",
    runtime_factories=[OrificeSetting(["ORIF"])],
    observation_builder=ObservationBuilder()
        .add_node_depths(["T1", "T2"])
        .add_link_flows(["OUT"]),
    reward_terms=[
        FloodingVolume(node_ids=["T1"]),
        PeakOutflow(link_ids=["OUT"]),
    ],
    control_interval_steps=4,  # advance 4 routing steps per env step
)
```

## Multi-objective configuration

Multi-objective envs require explicit `ideal_point` (best-case cost
vector) and `reference_point` (nadir / worst-case) for normalised
hypervolume scoring:

```python
from openswmm_gymnasium.envs import SwmmMORTCEnv

env = SwmmMORTCEnv(
    "path/to/model.inp",
    runtime_factories=[...],
    observation_builder=...,
    reward_terms=[
        FloodingVolume(node_ids=["T1"]),
        PeakOutflow(link_ids=["OUT"]),
    ],
    ideal_point=[0.0, 0.0],            # zero cost is best
    reference_point=[1.0e5, 50.0],     # generous worst-case
)
```

Both vectors must match the order and length of `reward_terms`. The
returned `info["mo_score"]` at episode termination is the normalised
hypervolume of the cumulative-cost vector in `[0, 1]`.