# `Monty.Experiment.RecognitionStrategy.RecognitionResult`
[🔗](https://github.com/d-led/ex.tbp.monty/blob/v0.1.0/packages/ex_tbp_monty_core/lib/monty/experiment/recognition_strategy/recognition_result.ex#L1)

What a policy decided: stop, time out the laggards, or start exploring.

Mirrors `tbp/monty/experiment/recognition_strategy.py::RecognitionResult`. Three
booleans, and the episode loop does three different things with them:

  * `is_done` — stop the episode.
  * `time_out_lms` — tell the learning modules that have not concluded to
    conclude (`:time_out`) before the episode ends, so the episode still has an
    answer to report.
  * `start_exploring` — switch the model into exploring mode, which is how a
    training episode keeps moving over an object it has already recognised.

# `t`

```elixir
@type t() :: %Monty.Experiment.RecognitionStrategy.RecognitionResult{
  is_done: boolean(),
  start_exploring: boolean(),
  time_out_lms: boolean()
}
```

---

*Consult [api-reference.md](api-reference.md) for complete listing*