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Explore Beyond the Boundary Using Entropic Information

arXiv 2607.29419 ↗First posted 31 Jul 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 31 Jul 2026.

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References · 23

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11 more
  1. 2018Residual Reinforcement Learning for Robot Controlcited by 242
  2. 2020RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environmentscited by 135
  3. 2021State Entropy Maximization with Random Encoders for Efficient Explorationcited by 107
  4. 2021Reward (Mis)design for Autonomous Drivingcited by 102
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  6. 2017Learning to select data for transfer learning with Bayesian Optimizationcited by 64
  7. 2019Leveraging exploration in off-policy algorithms via normalizing flowscited by 45
  8. 2020BeBold: Exploration Beyond the Boundary of Explored Regionscited by 32
  9. 2021A Max-Min Entropy Framework for Reinforcement Learningcited by 28
  10. 2021Off-Policy Reinforcement Learning with Delayed Rewardscited by 25
  11. 2023Go Beyond Imagination: Maximizing Episodic Reachability with World Modelscited by 3

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