CAMP: Counterexamples, Abstraction, MDPs, and Policy Refinement for Enhancing Safety, Stability, and Rewards in Reinforcement Learning
Reinforcement learning (RL) has demonstrated exceptional performance across various real-world applications such as autonomous driving, robotic control, and finance. However, challenges surrounding safety and stability continue to limit its practical deployment. Specifically, effectively blocking tr...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10945853/ |
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