I have the same issue when trying to log hparams metrics

@timothe-chaumont as you did the implementation in #984, could you have a look?

new_logger.dump()

I would expect dump(num_timesteps) there

You are right @rogierz, metric values that are passed to HParam through the metric_dict won't be saved. They are supposed to reference metrics that have been logged separately (otherwise they won't be displayed in HPARAMS).

In the documentation, the example mentions:

# define the metrics that will appear in the `HPARAMS` Tensorboard tab by referencing their tag
# Tensorbaord will find & display metrics from the `SCALARS` tab
metric_dict = {
    "rollout/ep_len_mean": 0,
    "train/value_loss": 0.0,
}

So in your example you would need to log your custom metric with

new_logger.record("missing_metric", 2.0)

so that, when referenced in HParam, TensorBoard will find it and add it to the HPARAMS tab:

IMHO I might have an idea where this bug comes from: E.g. in HumanOutputFormat one iterates over all keys to output during logger.dump. But the iteration happens together with key_excluded. It is to note that nowhere is it ensured that a loggers.name_to_value and name_to_exclude are the same length (they are public and one might want to use them to do more specific logging then possible just with log_value/mean)..

I would suggest the following patch @

- for (key, value), (_, excluded) in zip(sorted(key_values.items()), sorted(key_excluded.items())):
+ for (key, value) in sorted(key_values.items():
+     excluded = key_excluded.get(key, ('',))

Similar changes are needed, e.g., for the TensorboardOutputFormat @