When Maximum Entropy Misleads Policy Optimization

2025-06-05

AI Summary

Maximum entropy reinforcement learning (MaxEnt RL) compromises optimality in precision-critical continuous control tasks by excessively promoting stochasticity—high-entropy preferences misguide policy optimization, yielding inferior performance relative to non-MaxEnt methods. Method: We conduct systematic comparative experiments across standard continuous-control benchmarks, analyze policy entropy trajectories, and evaluate reward sensitivity to rigorously assess robustness implications of entropy maximization. Contribution/Results: This work provides the first empirical evidence that entropy maximization can undermine—not enhance—robustness in such settings. We introduce a novel “reward-design–entropy-constraint” dynamic trade-off perspective, advocating adaptive tuning of entropy regularization strength based on task-specific requirements (e.g., execution precision). Results show that on low-entropy-preferred tasks, MaxEnt algorithms—including SAC—significantly underperform non-MaxEnt counterparts such as TD3 and PPO, challenging the implicit assumption that entropy maximization is universally beneficial.

Technology Category

• Machine Learning: Reinforcement Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Analyzes MaxEnt RL's trade-off between robustness and optimality
• Investigates entropy maximization misleading policy optimization
• Explores balancing reward design and entropy in control tasks

Innovation

• Analyzes MaxEnt RL robustness-optimality trade-off
• Demonstrates entropy maximization misleading effects
• Balances reward design with entropy maximization