$q$-exponential family for policy optimization

ICLR 2025 Conference Submission5488 Authors

Keywords: reinforcement learning, policy optimization, $q$-exponential family, heavy-tailed policies, sparse policies

TL;DR: we explored a family of policy distributions that are promising alternatives to the standard Gaussian policy in policy optimization

Abstract:

Policy optimization methods benefit from a simple and tractable policy parametrization, usually the Gaussian for continuous action spaces. In this paper, we consider a broader policy family that remains tractable: the $q$-exponential family.
This family of policies is flexible, allowing the specification of both heavy-tailed policies ($q>1$) and light-tailed policies ($q<1$). This paper examines the interplay between $q$-exponential policies for several actor-critic algorithms conducted on both online and offline problems. We find that heavy-tailed policies are more effective in general and can consistently improve on Gaussian.
In particular, we find the Student's t-distribution to be more stable than the Gaussian across settings and that a heavy-tailed $q$-Gaussian for Tsallis Advantage Weighted Actor-Critic consistently performs well in offline benchmark problems.
In summary, we find that the Student's t policy a strong candidate for drop-in replacement to the Gaussian.

Primary Area: reinforcement learning

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Submission Number: 5488