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

MetricsLogger.set_value(key: str | Tuple[str, ...], value: Any, *, 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 = 0.05, reduce_per_index_on_aggregate: bool = False) → None[source]#

Overrides the logged values under key with value.

The internal values list under key is cleared and reset to [value]. If key already exists, this method will NOT alter the reduce settings. Otherwise, it will apply the provided reduce settings (reduce, window, ema_coeff, and clear_on_reduce).

Parameters:

  • key – The key to override.

  • value – The new value to set the internal values list to (will be set to a list containing a single item value).

  • 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()). Note that this is only applied if key does not exist in self yet.

  • 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”. Note that this is only applied if key does not exist in self yet.

  • 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 Note that this is only applied if key does not exist in self yet.

  • 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. Note that this is only applied if key does not exist in self yet.

  • 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: peeked_value, throuthput_per_sec = <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.

  • 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. Note that this is only applied if key does not exist in self yet.