Explore Beyond the Boundary Using Entropic Information

2026-07-31

AI Summary

This work addresses the challenge of exploration in reinforcement learning under sparse and delayed reward signals, where the lack of informative feedback hinders agents from discovering high-value states. To overcome this limitation, the authors propose ENTINEX, a novel method that leverages entropy-based information to precisely identify the boundary of the state visitation distribution and introduces an intrinsic reward specifically at these boundary regions to incentivize active exploration of unvisited areas. By integrating entropy-aware state distribution modeling, an intrinsic reward mechanism, and a deep reinforcement learning framework, ENTINEX consistently outperforms existing exploration strategies across a range of environments with sparse or delayed rewards, significantly improving both exploration efficiency and task performance.

Abstract

In reinforcement learning, exploration with sparse and delayed rewards presents a significant challenge due to the limited feedback available for guiding the learning process. Addressing this issue requires extensive exploration in the state space to discover valuable reward signals. In this paper, we propose Entropic Information for Exploration (ENTINEX), a novel method that enhances exploration by incentivizing agents to explore beyond the boundaries of the state distribution. ENTINEX achieves this by assigning intrinsic rewards to these boundaries, leveraging entropic information to identify them effectively. Through extensive experimentation, we demonstrate that ENTINEX consistently improves exploration performance in environments characterized by sparse and delayed rewards. Our experimental results show that ENTINEX outperforms existing exploration methods, highlighting its effectiveness in both sparse and delayed reward scenarios.

Problem

• reinforcement learning
• exploration
• sparse rewards
• delayed rewards
• state space

Innovation

• entropic information
• exploration
• intrinsic reward
• sparse rewards
• reinforcement learning