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arXiv · 2506.05615

When Maximum Entropy Misleads Policy Optimization

Ruipeng Zhang·Ya-Chien Chang·Sicun Gao
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Abstract

The Maximum Entropy Reinforcement Learning (MaxEnt RL) framework is a leading approach for achieving efficient learning and robust performance across many RL tasks. However, MaxEnt methods have also been shown to struggle with performance-critical control problems in practice, where non-MaxEnt algorithms can successfully learn. In this work, we analyze how the trade-off between robustness and optimality affects the performance of MaxEnt algorithms in complex control tasks: while entropy maximization enhances exploration and robustness, it can also mislead policy optimization, leading to failure in tasks that require precise, low-entropy policies. Through experiments on a variety of control problems, we concretely demonstrate this misleading effect. Our analysis leads to better understanding of how to balance reward design and entropy maximization in challenging control problems.

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Ruipeng Zhang, Ya-Chien Chang, Sicun Gao. 2025-06-05. When Maximum Entropy Misleads Policy Optimization. https://arxiv.org/abs/2506.05615

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