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

MetricsLogger.log_value(key: str | Tuple[str, ...], value: Any, *, reduce: str | None = None, window: int | float | None = None, ema_coeff: float | None = None, percentiles: List[int] | bool | None = None, clear_on_reduce: bool | None = -1, with_throughput: bool | None = None, throughput_ema_coeff: float | None = -1, reduce_per_index_on_aggregate: bool | None = -1, **kwargs: Dict[str, Any]) → None[source]#

Logs a new value or item under a (possibly nested) key to the logger.

Parameters:

  • key – The key (or nested key-tuple) to log the value under.

  • value – A numeric value, an item to log or a StatsObject containing multiple values to log.

  • reduce – The reduction method to apply when compiling metrics at the root logger. By default, the reduction methods to choose from here are the keys of rllib.utils.metrics.metrics_logger.DEFAULT_STATS_CLS_LOOKUP. You can provide your own reduce methods by extending rllib.utils.metrics.metrics_logger.DEFAULT_STATS_CLS_LOOKUP and passing it to AlgorithmConfig.logging()).

  • 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 Defaults to 0.01.

  • 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 – Deprecated. Use reduce=”lifetime_sum” instead. If True, all values under key will be cleared 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 supported by default only for reduce=sum and reduce=lifetime_sum.

  • throughput_ema_coeff – Deprecated argument. Throughput is not smoothed with ema anymore but calculate once per MetricsLogger.reduce() call.

  • reduce_per_index_on_aggregate – Deprecated argument. Aggregation now happens over all values of incoming stats objects once per MetricsLogger.reduce() call, treating each incoming value with equal weight.