Performance indicators

Performance indicators for multi-objective RL algorithms.

We mostly rely on pymoo for the computation of axiomatic indicators (HV and IGD), but some are customly made.

morl_baselines.common.performance_indicators.cardinality(front: List[ndarray]) → float

Cardinality Metric.

Cardinality of the Pareto front approximation.

Parameters:

front – current pareto front to compute the cardinality on

Returns:

float – cardinality metric

morl_baselines.common.performance_indicators.expected_utility(front: ~typing.List[~numpy.ndarray], weights_set: ~typing.List[~numpy.ndarray], utility: ~typing.Callable = <function dot>) → float

Expected Utility Metric.

Expected utility of the policies on the PF for various weights. Similar to R-Metrics in MOO. But only needs one PF approximation. Paper: L. M. Zintgraf, T. V. Kanters, D. M. Roijers, F. A. Oliehoek, and P. Beau, “Quality Assessment of MORL Algorithms: A Utility-Based Approach,” 2015.

Parameters:

  • front – current pareto front to compute the eum on

  • weights_set – weights to use for the utility computation

  • utility – utility function to use (default: dot product)

Returns:

float – eum metric

morl_baselines.common.performance_indicators.hypervolume(ref_point: ndarray, points: List[_SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]]) → float

Computes the hypervolume metric for a set of points (value vectors) and a reference point (from Pymoo).

Parameters:

  • ref_point (np.ndarray) – Reference point

  • points (List[np.ndarray]) – List of value vectors

Returns:

float – Hypervolume metric

morl_baselines.common.performance_indicators.igd(known_front: List[ndarray], current_estimate: List[ndarray]) → float

Inverted generational distance metric. Requires to know the optimal front.

Parameters:

  • known_front – known pareto front for the problem

  • current_estimate – current pareto front

Returns:

a float stating the average distance between a point in current_estimate and its nearest point in known_front

morl_baselines.common.performance_indicators.maximum_utility_loss(front: ~typing.List[~numpy.ndarray], reference_set: ~typing.List[~numpy.ndarray], weights_set: ~numpy.ndarray, utility: ~typing.Callable = <function dot>) → float

Maximum Utility Loss Metric.

Maximum utility loss of the policies on the PF for various weights. Paper: L. M. Zintgraf, T. V. Kanters, D. M. Roijers, F. A. Oliehoek, and P. Beau, “Quality Assessment of MORL Algorithms: A Utility-Based Approach,” 2015.

Parameters:

  • front – current pareto front to compute the mul on

  • reference_set – reference set (e.g. true Pareto front) to compute the mul on

  • weights_set – weights to use for the utility computation

  • utility – utility function to use (default: dot product)

Returns:

float – mul metric

morl_baselines.common.performance_indicators.sparsity(front: List[ndarray]) → float

Sparsity metric from PGMORL.

Basically, the sparsity is the average distance between each point in the front.

Parameters:

front – current pareto front to compute the sparsity on

Returns:

float – sparsity metric