Configuration

All runtime parameters for cobre run are controlled by config.json in the case directory. This page documents every section and field.

Minimal Config

{
  "version": "2.0.0",
  "training": {
    "forward_passes": 50,
    "stopping_rules": [{ "type": "iteration_limit", "limit": 100 }]
  }
}

All other sections are optional with defaults documented below.

`training`

Controls the SDDP training phase.

Mandatory Fields

Field	Type	Description
`forward_passes`	integer	Number of scenario trajectories per iteration. Larger values reduce variance in each iteration’s cut but increase cost per iteration.
`stopping_rules`	array	At least one stopping rule (see below). The rule set must contain at least one `iteration_limit` rule.

Optional Fields

Field	Type	Default	Description
`enabled`	boolean	`true`	Set to `false` to skip training and proceed directly to simulation (requires a pre-trained policy).
`seed`	integer	`42`	Random seed for reproducible scenario generation.
`stopping_mode`	`"any"` or `"all"`	`"any"`	How multiple stopping rules combine: `"any"` stops when the first rule is satisfied; `"all"` requires all rules to be satisfied simultaneously.

Stopping Rules

Each entry in stopping_rules is a JSON object with a "type" discriminator.

`iteration_limit`

Stop after a fixed number of training iterations.

{ "type": "iteration_limit", "limit": 200 }

Field	Type	Description
`limit`	integer	Maximum number of SDDP iterations to run.

`time_limit`

Stop after a wall-clock time budget is exhausted.

{ "type": "time_limit", "seconds": 3600.0 }

Field	Type	Description
`seconds`	float	Maximum training time in seconds.

`bound_stalling`

Stop when the relative improvement in the lower bound falls below a threshold.

{ "type": "bound_stalling", "iterations": 20, "tolerance": 0.0001 }

Field	Type	Description
`iterations`	integer	Window size: the number of past iterations over which to compute the relative improvement.
`tolerance`	float	Relative improvement threshold. Training stops when the improvement over the window is below this value.

`stopping_mode`

When multiple stopping rules are listed, stopping_mode controls how they combine:

"any" (default): stop when any one rule is satisfied.
"all": stop only when every rule is satisfied simultaneously.

{
  "training": {
    "forward_passes": 50,
    "stopping_mode": "all",
    "stopping_rules": [
      { "type": "iteration_limit", "limit": 500 },
      { "type": "bound_stalling", "iterations": 20, "tolerance": 0.0001 }
    ]
  }
}

`simulation`

Controls the optional post-training simulation phase.

Field	Type	Default	Description
`enabled`	boolean	`false`	Enable the simulation phase after training.
`num_scenarios`	integer	`2000`	Number of independent Monte Carlo simulation scenarios to evaluate.
`policy_type`	`"outer"`	`"outer"`	Policy representation for simulation. `"outer"` uses the cut pool (Benders cuts).

When simulation.enabled is false or num_scenarios is 0, the simulation phase is skipped regardless of the --skip-simulation flag.

Example:

{
  "simulation": {
    "enabled": true,
    "num_scenarios": 1000
  }
}

`policy`

Controls policy persistence (checkpoint saving and warm-start loading).

Field	Type	Default	Description
`path`	string	`"./policy"`	Directory where policy data (cuts, states) is stored.
`mode`	`"fresh"`, `"warm_start"`, or `"resume"`	`"fresh"`	Initialization mode. `"fresh"` starts from scratch; `"warm_start"` loads cuts from a previous run; `"resume"` continues an interrupted run.
`validate_compatibility`	boolean	`true`	When loading a policy, verify that entity counts, stage counts, and cut dimensions match the current system.

`exports`

Controls which outputs are written to the results directory.

Field	Type	Default	Description
`training`	boolean	`true`	Write training convergence data (Parquet).
`cuts`	boolean	`true`	Write the cut pool (FlatBuffers).
`states`	boolean	`true`	Write visited state vectors (Parquet).
`vertices`	boolean	`true`	Write inner approximation vertices when applicable (Parquet).
`simulation`	boolean	`true`	Write per-entity simulation results (Parquet).
`forward_detail`	boolean	`false`	Write per-scenario forward-pass detail (large; disabled by default).
`backward_detail`	boolean	`false`	Write per-scenario backward-pass detail (large; disabled by default).
`compression`	`"zstd"`, `"lz4"`, or `"none"`	`null`	Output Parquet compression algorithm. `null` uses the crate default (zstd).

Full Example

{
  "$schema": "https://cobre.dev/schemas/v2/config.schema.json",
  "version": "2.0.0",
  "training": {
    "seed": 42,
    "forward_passes": 50,
    "stopping_rules": [
      { "type": "iteration_limit", "limit": 200 },
      { "type": "bound_stalling", "iterations": 20, "tolerance": 0.0001 }
    ],
    "stopping_mode": "any"
  },
  "simulation": {
    "enabled": true,
    "num_scenarios": 2000
  },
  "policy": {
    "path": "./policy",
    "mode": "fresh"
  },
  "exports": {
    "training": true,
    "cuts": true,
    "states": true,
    "simulation": true,
    "compression": "zstd"
  }
}

Cobre