Is the Policy Gradient a Gradient?

TL;DR

This paper clarifies that most policy gradient methods do not optimize the discounted return and instead follow a direction that is not a true gradient, leading to potential convergence issues.

Contribution

It proves that common policy gradient updates are not true gradients and demonstrates their potential to converge to suboptimal fixed points, clarifying a longstanding confusion.

Findings

Most policy gradient updates are not true gradients.

Counterexample shows convergence to globally pessimal fixed points.

Widespread misunderstandings in literature are corrected.

Abstract

The policy gradient theorem describes the gradient of the expected discounted return with respect to an agent's policy parameters. However, most policy gradient methods drop the discount factor from the state distribution and therefore do not optimize the discounted objective. What do they optimize instead? This has been an open question for several years, and this lack of theoretical clarity has lead to an abundance of misstatements in the literature. We answer this question by proving that the update direction approximated by most methods is not the gradient of any function. Further, we argue that algorithms that follow this direction are not guaranteed to converge to a "reasonable" fixed point by constructing a counterexample wherein the fixed point is globally pessimal with respect to both the discounted and undiscounted objectives. We motivate this work by surveying the literature and showing that there remains a widespread misunderstanding regarding discounted policy gradient methods, with errors present even in highly-cited papers published at top conferences.