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Poster
On the Global Convergence Rates of Softmax Policy Gradient Methods
Jincheng Mei · Chenjun Xiao · Csaba Szepesvari · Dale Schuurmans

Thu Jul 16 09:00 AM -- 09:45 AM & Thu Jul 16 08:00 PM -- 08:45 PM (PDT) @ Virtual
in Poster Session 44 »
We make three contributions toward better understanding policy gradient methods in the tabular setting. First, we show that with the true gradient, policy gradient with a softmax parametrization converges at a $O(1/t)$ rate, with constants depending on the problem and initialization. This result significantly expands the recent asymptotic convergence results. The analysis relies on two findings: that the softmax policy gradient satisfies a \L{}ojasiewicz inequality, and the minimum probability of an optimal action during optimization can be bounded in terms of its initial value. Second, we analyze entropy regularized policy gradient and show that it enjoys a significantly faster linear convergence rate $O(e^{-t})$ toward softmax optimal policy. This result resolves an open question in the recent literature. Finally, combining the above two results and additional new $\Omega(1/t)$ lower bound results, we explain how entropy regularization improves policy optimization, even with the true gradient, from the perspective of convergence rate. The separation of rates is further explained using the notion of non-uniform \L{}ojasiewicz degree. These results provide a theoretical understanding of the impact of entropy and corroborate existing empirical studies.
Keywords: Reinforcement Learning Theory · Reinforcement Learning - Theory
[ Slides] 

Author Information

Jincheng Mei (University of Alberta / Google Brain)
Chenjun Xiao (Google / University of Alberta)
Csaba Szepesvari (DeepMind/University of Alberta)
Dale Schuurmans (University of Alberta)

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