ray.rllib.utils.metrics.metrics_logger.MetricsLogger.log_dict#

MetricsLogger.log_dict(value_dict, *, key: str | Tuple[str, ...] | None = None, reduce: str | None = 'mean', window: int | float | None = None, ema_coeff: float | None = None, percentiles: List[int] | bool = False, clear_on_reduce: bool = False, with_throughput: bool = False, throughput_ema_coeff: float | None = None, reduce_per_index_on_aggregate: bool = False) → None[source]#

Logs all leafs of a possibly nested dict of values to this logger.

To aggregate logs from upstream components, use aggregate.

This is a convinience function that is equivalent to: ` tree.map_structure_with_path(lambda path, value: logger.log_value(path, value, ...), value_dict) `

Traverses through all leafs of stats_dict and - if a path cannot be found in this logger yet, will add the Stats found at the leaf under that new key. If a path already exists, will merge the found leaf (Stats) with the ones already logged before. This way, stats_dict does NOT have to have the same structure as what has already been logged to self, but can be used to log values under new keys or nested key paths.

logger = MetricsLogger()

# Log n dicts with keys "a" and (some) "b". By default, all logged values
# under that key are averaged, once `reduce()` is called.
logger.log_dict(
    {
        "a": 0.1,
        "b": -0.1,
    },
    window=10,
)
logger.log_dict({
    "b": -0.2,
})  # don't have to repeat `window` arg if key already exists
logger.log_dict({
    "a": 0.2,
    "c": {"d": 5.0},  # can also introduce an entirely new (nested) key
})

# Peek at the current (reduced) values under "a" and "b".
check(logger.peek("a"), 0.15)
check(logger.peek("b"), -0.15)
check(logger.peek(("c", "d")), 5.0)

# Reduced all stats.
results = logger.reduce()
check(results, {
    "a": 0.15,
    "b": -0.15,
    "c": {"d": 5.0},
})
Parameters:

  • value_dict – The (possibly nested) dict with individual values as leafs to be logged to this logger.

  • key – An additional key (or tuple of keys) to prepend to all the keys (or tuples of keys in case of nesting) found inside stats_dict. Useful to log the entire contents of stats_dict in a more organized fashion under one new key, for example logging the results returned by an EnvRunner under key

  • reduce – The reduction method to apply, once self.reduce() is called. If None, will collect all logged values under key in a list (and also return that list upon calling self.reduce()).

  • window – An optional window size to reduce over. If not None, then the reduction operation is only applied to the most recent window items, and - after reduction - the internal values list under key is shortened to hold at most window items (the most recent ones). Must be None if ema_coeff is provided. If None (and ema_coeff is None), reduction must not be “mean”.

  • ema_coeff – An optional EMA coefficient to use if reduce is “mean” and no window is provided. Note that if both window and ema_coeff are provided, an error is thrown. Also, if ema_coeff is provided, reduce must be “mean”. The reduction formula for EMA is: EMA(t1) = (1.0 - ema_coeff) * EMA(t0) + ema_coeff * new_value

  • percentiles – If reduce is None, we can compute the percentiles of the values list given by percentiles. Defaults to [0, 0.5, 0.75, 0.9, 0.95, 0.99, 1] if set to True. When using percentiles, a window must be provided. This window should be chosen carefully. RLlib computes exact percentiles and the computational complexity is O(m*n*log(n/m)) where n is the window size and m is the number of parallel metrics loggers involved (for example, m EnvRunners).

  • clear_on_reduce – If True, all values under key will be emptied after self.reduce() is called. Setting this to True is useful for cases, in which the internal values list would otherwise grow indefinitely, for example if reduce is None and there is no window provided.

  • with_throughput – Whether to track a throughput estimate together with this metric. This is only supported for reduce=sum and clear_on_reduce=False metrics (aka. “lifetime counts”). The Stats object under the logged key then keeps track of the time passed between two consecutive calls to reduce() and update its throughput estimate. The current throughput estimate of a key can be obtained through: <MetricsLogger>.peek(key, throughput=True).

  • throughput_ema_coeff – The EMA coefficient to use for throughput tracking. Only used if with_throughput=True. Defaults to 0.05 if with_throughput is True.

  • reduce_per_index_on_aggregate – If True, when merging Stats objects, we reduce incoming values per index such that the new value at index n will be the reduced value of all incoming values at index n. If False, when reducing n Stats, the first n merged values will be the reduced value of all incoming values at index 0, the next n merged values will be the reduced values of all incoming values at index 1, etc.