Revisiting stochastic off-policy action-value gradients

Yemi Okesanjo; Victor Kofia

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arXiv · 1703.02102

Revisiting stochastic off-policy action-value gradients

Yemi Okesanjo·Victor Kofia

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Abstract

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.

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BibTeX RIS

Yemi Okesanjo, Victor Kofia. 2017-03-06. Revisiting stochastic off-policy action-value gradients. https://arxiv.org/abs/1703.02102

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