ENTINEX: New Method for Sparse Reward Exploration in Reinforcement Learning
A new approach called Entropic Information for Exploration (ENTINEX) has been developed by researchers to tackle the difficulties of exploration in reinforcement learning settings characterized by sparse and delayed rewards. This technique encourages agents to venture outside the limits of the state distribution by providing intrinsic rewards for these edges, which are determined through entropic information. Comprehensive testing shows that ENTINEX significantly enhances exploration capabilities and surpasses current exploration strategies in situations with sparse and delayed rewards. The research can be accessed on arXiv with the identifier 2607.29419.
Key facts
- ENTINEX is a novel method for exploration in reinforcement learning.
- It targets environments with sparse and delayed rewards.
- It assigns intrinsic rewards to boundaries of the state distribution.
- It uses entropic information to identify these boundaries.
- Experiments show consistent improvement in exploration performance.
- ENTINEX outperforms existing exploration methods.
- The paper is available on arXiv (2607.29419).
Entities
Institutions
- arXiv