scope_rl.ope.discrete.cumulative_distribution_estimators.CumulativeDistributionSNTIS#

class scope_rl.ope.discrete.cumulative_distribution_estimators.CumulativeDistributionSNTIS(estimator_name='cdf_sntis')[source]#

Self Normalized Trajectory-wise Importance Sampling (SNTIS) for estimating the cumulative distribution function (CDF) for discrete action spaces.

Bases: scope_rl.ope.discrete.CumulativeDistributionTIS scope_rl.ope.BaseCumulativeDistributionOPEEstimator

Imported as: scope_rl.ope.discrete.CumulativeDistributionSNTIS

Note

SNTIS estimates the CDF via trajectory-wise importance weighting as follows.

\[\hat{F}_{\mathrm{SNTIS}}(m, \pi; \mathcal{D})) := \sum_{i=1}^n \frac{w_{0:T-1}^{(i)}}{\sum_{i'=1}^n w_{0:T-1}^{(i')}} \mathbb{I} \left \{\sum_{t=0}^{T-1} \gamma^t r_t^{(i)} \leq m \right \}\]

where \(\hat{F}(\cdot)\) is the estimated cumulative distribution function, \(w_{0:T-1} := \prod_{t=0}^{T-1} (\pi(a_t \mid s_t) / \pi_0(a_t \mid s_t))\) is the trajectory-wise importance weight, and \(\mathbb{I} \{ \cdot \}\) is the indicator function.

The self-normalized estimator is no longer unbiased, but has a bounded variance while also remaining consistent.

Parameters:

estimator_name (str, default="cdf_sntis") – Name of the estimator.

References

Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik Learned-Miller, Emma Brunskill, and Philip S. Thomas. “Universal Off-Policy Evaluation.” 2021.

Audrey Huang, Liu Leqi, Zachary C. Lipton, and Kamyar Azizzadenesheli. “Off-Policy Risk Assessment in Contextual Bandits.” 2021.

Methods

estimate_conditional_value_at_risk(...[, ...])

Estimate conditional value at risk.

estimate_cumulative_distribution_function(...)

Estimate the cumulative distribution function (CDF) of the reward distribution under the evaluation policy.

estimate_interquartile_range(...[, gamma, alpha])

Estimate interquartile range.

estimate_mean(step_per_trajectory, action, ...)

Estimate mean.

estimate_variance(step_per_trajectory, ...)

Estimate variance.
estimate_cumulative_distribution_function(step_per_trajectory, action, reward, pscore, evaluation_policy_action_dist, reward_scale, gamma=1.0, **kwargs)[source]#

Estimate the cumulative distribution function (CDF) of the reward distribution under the evaluation policy.

Parameters:

  • step_per_trajectory (int (> 0)) – Number of timesteps in an episode.

  • action (array-like of shape (n_trajectories * step_per_trajectory, )) – Action chosen by the behavior policy.

  • reward (array-like of shape (n_trajectories * step_per_trajectory, )) – Observed immediate rewards.

  • pscore (array-like of shape (n_trajectories * step_per_trajectory, )) – Conditional action choice probability of the behavior policy, i.e., \(\pi_0(a \mid s)\)

  • evaluation_policy_action_dist (array-like of shape (n_trajectories * step_per_trajectory, n_action)) – Conditional action distribution induced by the evaluation policy, i.e., \(\pi(a \mid s_t) \forall a \in \mathcal{A}\)

  • reward_scale (array-like of shape (n_partition, )) – Scale of the trajectory-wise reward used for x-axis of the CDF plot.

  • gamma (float, default=1.0) – Discount factor. The value should be within (0, 1].

Returns:

estimated_cumulative_distribution_function – Estimated cumulative distribution function for the pre-defined reward scale.

Return type:

ndarray of shape (n_partition, ) or (n_episode, )

estimate_conditional_value_at_risk(step_per_trajectory, action, reward, pscore, evaluation_policy_action_dist, reward_scale, gamma=1.0, alphas=None, **kwargs)#

Estimate conditional value at risk.

Parameters:

  • step_per_trajectory (int (> 0)) – Number of timesteps in an episode.

  • action (array-like of shape (n_trajectories * step_per_trajectory, )) – Action chosen by the behavior policy.

  • reward (array-like of shape (n_trajectories * step_per_trajectory, )) – Observed immediate rewards.

  • pscore (array-like of shape (n_trajectories * step_per_trajectory, )) – Conditional action choice probability of the behavior policy, i.e., \(\pi_0(a \mid s)\)

  • evaluation_policy_action_dist (array-like of shape (n_trajectories * step_per_trajectory, n_action)) – Conditional action distribution induced by the evaluation policy, i.e., \(\pi(a \mid s_t) \forall a \in \mathcal{A}\)

  • reward_scale (array-like of shape (n_partition, )) – Scale of the trajectory-wise reward used for x-axis of the CDF plot.

  • gamma (float, default=1.0) – Discount factor. The value should be within (0, 1].

  • alphas (array-like of shape (n_alpha, ), default=None) – Set of proportions of the shaded region. The values should be within [0, 1). If None is given, np.linspace(0, 1, 21) will be used.

Returns:

estimated_conditional_value_at_risk – Estimated conditional value at risk (CVaR) of the reward under the evaluation policy.

Return type:

ndarray of (n_alpha, )

estimate_interquartile_range(step_per_trajectory, action, reward, pscore, evaluation_policy_action_dist, reward_scale, gamma=1.0, alpha=0.05, **kwargs)#

Estimate interquartile range.

Parameters:

  • step_per_trajectory (int (> 0)) – Number of timesteps in an episode.

  • action (array-like of shape (n_trajectories * step_per_trajectory, )) – Action chosen by the behavior policy.

  • reward (array-like of shape (n_trajectories * step_per_trajectory, )) – Observed immediate rewards.

  • pscore (array-like of shape (n_trajectories * step_per_trajectory, )) – Conditional action choice probability of the behavior policy, i.e., \(\pi_0(a \mid s)\)

  • evaluation_policy_action_dist (array-like of shape (n_trajectories * step_per_trajectory, n_action)) – Conditional action distribution induced by the evaluation policy, i.e., \(\pi(a \mid s_t) \forall a \in \mathcal{A}\)

  • reward_scale (array-like of shape (n_partition, )) – Scale of the trajectory-wise reward used for x-axis of the CDF plot.

  • gamma (float, default=1.0) – Discount factor. The value should be within (0, 1].

  • alpha (float, default=0.05) – Proportion of the shaded region.

Returns:

estimated_interquartile_range – Estimated interquartile range of the reward under the evaluation policy.

key: [
    mean,
    {100 * (1. - alpha)}% quartile (lower),
    {100 * (1. - alpha)}% quartile (upper),
]

Return type:

dict

estimate_mean(step_per_trajectory, action, reward, pscore, evaluation_policy_action_dist, reward_scale, gamma=1.0, **kwargs)#

Estimate mean.

Parameters:

  • step_per_trajectory (int (> 0)) – Number of timesteps in an episode.

  • action (array-like of shape (n_trajectories * step_per_trajectory, )) – Action chosen by the behavior policy.

  • reward (array-like of shape (n_trajectories * step_per_trajectory, )) – Observed immediate rewards.

  • pscore (array-like of shape (n_trajectories * step_per_trajectory, )) – Conditional action choice probability of the behavior policy, i.e., \(\pi_0(a \mid s)\)

  • evaluation_policy_action_dist (array-like of shape (n_trajectories * step_per_trajectory, n_action)) – Conditional action distribution induced by the evaluation policy, i.e., \(\pi(a \mid s_t) \forall a \in \mathcal{A}\)

  • reward_scale (array-like of shape (n_partition, )) – Scale of the trajectory-wise reward used for x-axis of the CDF plot.

  • gamma (float, default=1.0) – Discount factor. The value should be within (0, 1].

Returns:

estimated_mean – Estimated mean of the reward under the evaluation policy.

Return type:

float

estimate_variance(step_per_trajectory, action, reward, pscore, evaluation_policy_action_dist, reward_scale, gamma=1.0, **kwargs)#

Estimate variance.

Parameters:

  • step_per_trajectory (int (> 0)) – Number of timesteps in an episode.

  • action (array-like of shape (n_trajectories * step_per_trajectory, )) – Action chosen by the behavior policy.

  • reward (array-like of shape (n_trajectories * step_per_trajectory, )) – Observed immediate rewards.

  • pscore (array-like of shape (n_trajectories * step_per_trajectory, )) – Conditional action choice probability of the behavior policy, i.e., \(\pi_0(a \mid s)\)

  • evaluation_policy_action_dist (array-like of shape (n_trajectories * step_per_trajectory, n_action)) – Conditional action distribution induced by the evaluation policy, i.e., \(\pi(a \mid s_t) \forall a \in \mathcal{A}\)

  • reward_scale (array-like of shape (n_partition, )) – Scale of the trajectory-wise reward used for x-axis of the CDF plot.

  • gamma (float, default=1.0) – Discount factor. The value should be within (0, 1].

Returns:

estimated_variance – Estimated variance of the reward under the evaluation policy.

Return type:

float

Methods