Solve distributionally robust markov policy

What it's for

Chooses recurring management or system actions that remain useful when learned transition probabilities are plausibly wrong.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "actions": [
    {
      "cost": 0,
      "id": "observe",
      "transition_counts": {
        "degraded": {
          "degraded": 140,
          "healthy": 10,
          "stable": 50
        },
        "healthy": {
          "degraded": 20,
          "healthy": 140,
          "stable": 40
        },
        "stable": {
          "degraded": 40,
          "healthy": 40,
          "stable": 120
        }
      }
    },
    {
      "cost": 2,
      "id": "intervene",
      "transition_counts": {
        "degraded": {
          "degraded": 20,
          "healthy": 120,
          "stable": 60
        },
        "healthy": {
          "degraded": 10,
          "healthy": 170,
          "stable": 20
        },
        "stable": {
          "degraded": 10,
          "healthy": 140,
          "stable": 50
        }
      }
    }

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "States are Markov-sufficient, rewards and action costs are stationary and additive, and transition counts represent the deployment population.",
    "Each state-action transition distribution lies independently inside its simultaneous L1 confidence ball; this rectangular ambiguity is conservative and excludes correlated model error.",
    "The robust Bellman update protects against transition sampling error represented by the ambiguity set, not omitted states, invalid rewards, or structural regime change.",
    "Policies govern reversible team-, project-, or system-level interventions and must not automate individual employment or surveillance decisions."
  ],
  "convergence": {
    "bellman_residual": 9.62e-10,
    "nominal_iterations": 444,
    "robust_iterations": 440,
    "tolerance": 1e-9
  },
  "decision": "robust_markov_policy_available",
  "method": "rectangular_l1_distributionally_robust_mdp_v1",
  "policy": [
    {
      "ambiguity_cost": 29.508,
      "nominal_action": "intervene",
      "nominal_value": 137.8174,
      "policy_changed_by_robustness": false,
      "robust_action": "intervene",
      "robust_value": 108.3094,
      "state_id": "degraded"
    },
    {
      "ambiguity_cost": 29.508,
      "nominal_action": "intervene",
      "nominal_value": 143.7369,
      "policy_changed_by_robustness": false,
      "robust_action": "intervene",
      "robust_value": 114.2289,
      "state_id": "stable"
    },
    {
      "ambiguity_cost": 29.508,
      "nominal_action": "observe",
      "nominal_value": 150.4557,
      "policy_changed_by_robustness": false,
      "robust_action": "observe",
      "robust_value": 120.9477,
      "state_id": "healthy"
    }
  ],

How it works

Markov & state-space control — Choose a policy over evolving states when today's action changes tomorrow's options.

1. 1 Solve a discounted Markov policy against simultaneous L1 transition-confidence sets derived from empirical state-action counts.
2. 2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "solve a discounted markov policy against" }
  → finds "solve_distributionally_robust_markov_policy"

gitrevio_capability_describe
  { "capability_id": "solve_distributionally_robust_markov_policy" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "solve_distributionally_robust_markov_policy", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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