When Maximum Entropy Misleads Policy Optimization

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.

Many AI systems learn by rewarding good actions while also encouraging randomness, a technique known as “maximum entropy” learning. This randomness helps AI explore new strategies and be more fault-tolerant, but it can backfire when a task demands very precise, consistent actions—such as drone control or guiding a four-legged robot. To understand this trade-off, we tested entropy-based algorithms on several control challenges and discovered that forcing too much randomness can actually mislead the learning process, causing the AI to miss the exact movements it needs. By examining these failures in detail, we pinpointed how and why entropy maximization can override the drive for precision. Our results provide AI researchers with guidance on developing training strategies that balance exploration and utility in real-world control scenarios.