Recycling Robot

The recycling-robot Markov decision process. From a high or low battery state the robot picks an action under a Bold or Timid policy: search (best reward, may drain the battery), wait (safe, smaller reward) or recharge. Each action transitions to a short outcome state whose probabilities encode the environment's response — finding cans, or being rescued at a steep penalty — so the stochastic transition function is expressed directly in the model.

A minimal, exact MDP for studying policy under risk. The two-stage action→outcome encoding is a reusable pattern for any stochastic transition, and the Bold-versus-Timid reward gap is visible directly in the exported reward and rescue counters.

# recycling-robot.osi.yaml — emitted automatically
semantic_model:
  name: "recycling-robot"
  source: "duckdb://recycling-robot.db"
  entities:
    - name: robot
      primary_key: id
  dimensions:
    - name: state
      type: categorical
    - name: t
      type: time
  measures:
    - name: row_count
      agg: count
    - name: active
      agg: sum
      filter: "state = 'ACTIVE'"