Imagine to Ensure Safety in Hierarchical Reinforcement Learning
This work investigates the safe exploration problem in reinforcement learning, where an agent must maximize cumulative performance while simultaneously satisfying safety constraints. This challenge becomes even more pronounced in long-horizon tasks, where existing safe methods face fundamental limitations due to compounding estimation errors and restricted exploration capabilities. To address this problem, we propose a method that combines a learnable world model with two complementary policies a high-level policy and a low-level policy to promote safety at both hierarchical levels. The high-level policy generates intermediate subgoals that bias exploration toward safe regions, while the ...
| Authors | Gregory Gorbov, Artem Latyshev, Aleksandr I. Panov |
|---|---|
| Primary category | cs.AI |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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