Objectives

class tensorforce.core.objectives.DeterministicPolicyGradient(name, summary_labels=None)

Deterministic policy gradient objective (specification key: det_policy_gradient).

Parameters:

• name (string) – Module name (internal use).
• summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).

class tensorforce.core.objectives.Plus(name, objective1, objective2, summary_labels=None)

Additive combination of two objectives (specification key: plus).

Parameters:

• name (string) – Module name (internal use).
• objective1 (specification) – First objective configuration (required).
• objective2 (specification) – Second objective configuration (required).
• summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).

class tensorforce.core.objectives.PolicyGradient(name, ratio_based=False, clipping_value=0.0, early_reduce=False, summary_labels=None)

Policy gradient objective, which maximizes the log-likelihood or likelihood-ratio scaled by the target reward value (specification key: policy_gradient).

Parameters:

• name (string) – Module name (internal use).
• ratio_based (bool) – Whether to scale the likelihood-ratio instead of the log-likelihood (default: false).
• clipping_value (parameter, float > 0.0) – Clipping threshold for the maximized value (default: no clipping).
• early_reduce (bool) – Whether to compute objective for reduced likelihoods instead of per likelihood (default: false).
• summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).

class tensorforce.core.objectives.Value(name, value='state', huber_loss=0.0, early_reduce=False, summary_labels=None)

Value approximation objective, which minimizes the L2-distance between the state-(action-)value estimate and the target reward value (specification key: value).

Parameters:

• name (string) – Module name (internal use).
• value ("state" | "action") – Whether to approximate the state- or state-action-value (default: “state”).
• huber_loss (parameter, float > 0.0) – Huber loss threshold (default: no huber loss).
• early_reduce (bool) – Whether to compute objective for reduced values instead of value per action (default: false).
• summary_labels ('all' | iter[string]) – Labels of summaries to record (default: inherit value of parent module).