Operator Lifecycle¶

Operators have two distinct execution modes: Manual Mode for interactive exploration and Script Mode for automated experiments. Each mode has its own state machine and signal flow.

Execution Modes¶

Mode	Description	Entry Point
Manual Mode	User clicks “Reset All” and “Step All” buttons. One step per click.	Operators Tab > Manual sub-tab
Script Mode	User loads a Python script defining operators and seeds. Execution runs automatically across N episodes.	Operators Tab > Script Experiments sub-tab

Important

Manual Mode and Script Mode are fully decoupled, they use separate signal paths and state machines. Running a script does not interfere with manual controls, and vice versa.

Manual Mode¶

State Machine¶

        %%{init: {"flowchart": {"curve": "linear"}} }%%
graph TD
    START(( )) -->|Operator configured| IDLE
    IDLE -->|Start All clicked| LAUNCHING
    LAUNCHING -->|Subprocess spawned| RUNNING
    RUNNING -->|Step All clicked| STEPPING
    STEPPING -->|Response received| RUNNING
    RUNNING -->|Reset All clicked| RESETTING
    RESETTING -->|Ready response| RUNNING
    RUNNING -->|Stop All clicked| STOPPED
    STEPPING -->|Stop All clicked| STOPPED
    STOPPED --> END(( ))

    style IDLE fill:#6c757d,stroke:#495057,color:#fff
    style LAUNCHING fill:#0d6efd,stroke:#0a58ca,color:#fff
    style RUNNING fill:#198754,stroke:#146c43,color:#fff
    style STEPPING fill:#ffc107,stroke:#cc9a06,color:#000
    style RESETTING fill:#0dcaf0,stroke:#0aa2c0,color:#000
    style STOPPED fill:#dc3545,stroke:#b02a37,color:#fff

Script Mode¶

Script mode enables automated multi-episode experiments. The user loads a Python script that defines operator configurations and seed lists, then the OperatorScriptExecutionManager drives execution automatically.

State Machine¶

        %%{init: {"flowchart": {"curve": "linear"}} }%%
graph TD
    START(( )) -->|Script loaded| IDLE
    IDLE -->|Run Experiment clicked| LAUNCHING
    LAUNCHING -->|Subprocess spawned + reset sent| WAITING_READY
    WAITING_READY -->|ready response| STEPPING

    subgraph LOOP ["Paced Step Loop"]
        STEPPING -->|episode_end response| EPISODE_DONE
    end

    EPISODE_DONE -->|All episodes finished| COMPLETED
    EPISODE_DONE -->|Next episode: reset with next seed| WAITING_READY

    WAITING_READY -->|User clicks Stop| STOPPED
    STEPPING -->|User clicks Stop| STOPPED

    style IDLE fill:#6c757d,stroke:#495057,color:#fff
    style LAUNCHING fill:#0d6efd,stroke:#0a58ca,color:#fff
    style WAITING_READY fill:#ffc107,stroke:#cc9a06,color:#000
    style STEPPING fill:#198754,stroke:#146c43,color:#fff
    style EPISODE_DONE fill:#0dcaf0,stroke:#0aa2c0,color:#000
    style COMPLETED fill:#198754,stroke:#146c43,color:#fff
    style STOPPED fill:#dc3545,stroke:#b02a37,color:#fff
    style LOOP fill:none,stroke:#555,stroke-dasharray: 5 5

OperatorScriptExecutionManager¶

The execution manager is a QObject state machine that owns the entire automatic execution lifecycle:

class OperatorScriptExecutionManager(QObject):
    # Signals sent to MainWindow for subprocess control
    launch_operator = pyqtSignal(str, object, int)   # op_id, config, seed
    reset_operator = pyqtSignal(str, int)             # op_id, seed
    step_operator = pyqtSignal(str)                   # op_id
    stop_operator = pyqtSignal(str)                   # op_id

    # Signals sent to ScriptExperimentWidget for UI updates
    progress_updated = pyqtSignal(int, int, int)      # episode, total, seed
    experiment_completed = pyqtSignal(int)             # num_episodes

The manager coordinates with MainWindow through signals:

Script Mode Signal Flow — Signal flow between the four components: ScriptExperimentWidget triggers `start_experiment()`, the Manager emits `launch_operator` and `step_operator` signals to MainWindow, which spawns the subprocess and relays `send_reset()` / `send_step()` commands. Responses flow back via stdout, and progress is reported to the UI via `progress_updated`.¶

Paced Stepping¶

A critical design detail: after receiving a step response, the manager does not immediately request the next step. Instead, it uses QTimer.singleShot(delay_ms, callback) to insert a small delay:

def on_step_received(self, operator_id: str) -> None:
    if not self._is_running or not self._waiting_for_response:
        return

    def _emit_next_step():
        if self._is_running and self._waiting_for_response:
            self.step_operator.emit(operator_id)

    QTimer.singleShot(self._step_delay_ms, _emit_next_step)

This delay serves a critical purpose: without it, the step loop runs so fast that Qt paint events are starved. The render view would show visual jumps (e.g., step 26 to step 37) because frames are logically rendered but never visually painted by the Qt compositor.

Approach	Speed	Visual Quality	Status
Timer spam	Uncontrolled	Broken (flooding)	Abandoned
Immediate re-emit	Maximum	Jittery (skips frames)	Abandoned
Paced stepping	Configurable	Smooth	Current

The step delay is configurable via a UI spinbox:

0 ms: fastest (may skip frames visually)
50 ms: smooth frame-by-frame rendering (default)
200 ms: slow-motion playback for analysis
1000 ms: one step per second

Warning

Never use QApplication.processEvents() for step pacing. It causes reentrancy issues and unpredictable behavior. QTimer.singleShot is the Qt-idiomatic solution.

Episode Advancement¶

When an episode ends, the manager advances to the next seed:

def on_episode_ended(
    self, operator_id: str, terminated: bool, truncated: bool
) -> None:
    self._current_episode += 1

    if self._current_episode >= self._total_episodes:
        self.experiment_completed.emit(self._total_episodes)
        return

    next_seed = self._seeds[self._current_episode]
    self.reset_operator.emit(operator_id, next_seed)

Seed modes:

Procedural: different seed per episode (tests generalization)
Fixed: same seed every episode (isolates agent behavior)

Script Format¶

Experiment scripts are Python files that define operator configurations and execution parameters:

# simple_random_baseline.py

operators = [
    {
        "id": "random_1",
        "name": "Random Agent",
        "type": "baseline",
        "worker_id": "operators_worker",
        "env_name": "minigrid",
        "task": "MiniGrid-Empty-8x8-v0",
    },
]

execution = {
    "num_episodes": 10,
    "seeds": [1000, 1001, 1002, 1003, 1004,
              1005, 1006, 1007, 1008, 1009],
    "step_delay_ms": 50,
    "env_mode": "procedural",
}

Scripts are loaded via compile() + sandboxed namespace execution (not importlib, which can cause import chain hangs on Linux):

with open(script_path) as f:
    code = compile(f.read(), script_path, "exec")
namespace = {}
exec(code, namespace)
operators = namespace["operators"]
execution = namespace.get("execution", {})

Telemetry Output¶

Operator telemetry is written to var/operators/telemetry/:

var/operators/telemetry/
├── random_minigrid_{run_id}_steps.jsonl    # Per-step data
└── random_minigrid_{run_id}_episodes.jsonl # Per-episode summaries

Each line is a JSON object with step index, action, reward, termination status, and optional render payload.