  Off-policy stochastic actor-critic methods rely on approximating the
stochastic policy gradient in order to derive an optimal policy. One may also
derive the optimal policy by approximating the action-value gradient. The use
of action-value gradients is desirable as policy improvement occurs along the
direction of steepest ascent. This has been studied extensively within the
context of natural gradient actor-critic algorithms and more recently within
the context of deterministic policy gradients. In this paper we briefly discuss
the off-policy stochastic counterpart to deterministic action-value gradients,
as well as an incremental approach for following the policy gradient in lieu of
the natural gradient.

Machine Learning;Machine Learning