---
title: "Reexamining the Exploration–Exploitation Dilemma from an Entropy-Driven Perspective"
authors:
  - "Renye Yan"
  - "Yaozhong Gan"
  - "Jikang Cheng"
  - "Yi Sun"
  - "Zongwei Wang"
  - "Ling Liang"
  - "Yimao Cai"
doi: "10.24963/ijcai.2026/571"
url: "https://ijcai.org/proceedings/2026/571"
pdf: "https://ijcai.org/proceedings/2026/0571.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "5128-5136"
track: "Main Track"
keywords:
  - "Machine Learning: Reinforcement learning"
---

# Reexamining the Exploration–Exploitation Dilemma from an Entropy-Driven Perspective

Renye Yan, Yaozhong Gan, Jikang Cheng, Yi Sun, Zongwei Wang, Ling Liang, Yimao Cai

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 5128-5136. DOI: [10.24963/ijcai.2026/571](https://doi.org/10.24963/ijcai.2026/571)

## Abstract

Achieving an optimal balance between exploration and exploitation remains a fundamental challenge in reinforcement learning. This work revisits the exploration-exploitation dilemma through the lens of entropy, offering a novel perspective on this enduring problem. It establishes a theoretical connection between policy's entropy and exploratory behavior, using entropy as a rational measure to quantify the exploration-exploitation trade-off. Theoretical analyses demonstrate that a modified Bellman equation, augmented with a novelty-seeking term, ensures appropriate entropy adjustment and guarantees its globally monotonic decay. The derived policy optimization process inherently accommodates all three regimes of the exploration-exploitation spectrum, enabling a principled transition from exploration to exploitation. Building on these theoretical insights, this work introduces AdaZero, an adaptive deep architecture that dynamically balances exploration and exploitation. Extensive empirical evaluations highlight AdaZero's robust performance and validate the theory's feasibility.