When Maximum Entropy Misleads Policy Optimization

TL;DR

This paper investigates how the MaxEnt RL framework, while promoting exploration and robustness, can sometimes mislead policy optimization, resulting in failures in tasks demanding precise control, and offers insights into balancing entropy and reward.

Contribution

The paper provides a detailed analysis of the limitations of MaxEnt RL in complex control tasks and demonstrates the misleading effects of entropy maximization through experiments.

Findings

MaxEnt RL can mislead policy optimization in low-entropy, precision tasks.

Entropy maximization enhances exploration but may hinder optimal policy learning.

Balancing reward and entropy is crucial for success in challenging control problems.

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.