Research map / Reinforcement learning / Policy gradient and actor-critic
Safe Model-based Reinforcement Learning with Stability Guarantees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SafeLyapunovLearning to update the policy from safe data | Inverted pendulum · Inverted pendulum | control performance | Helped |
| Collect exploratory data only within the current estimated region of attraction | SafeLyapunovLearning · Inverted pendulum | stability during learning | Helped |
| Use the GP confidence bound and Lyapunov decrease condition to certify the region of attraction | SafeLyapunovLearning · Inverted pendulum | region-of-attraction safety | Helped |
Closest papers by meaning
- Verified Safe Reinforcement Learning for Neural Network Dynamic Models (NeurIPS 2024 · similarity 0.90)
- Sampling-Based Safe Reinforcement Learning (NeurIPS 2026 · similarity 0.89)
- Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions (NeurIPS 2025 · similarity 0.88)
- Safely Learning Controlled Stochastic Dynamics (NeurIPS 2025 · similarity 0.88)
- Probabilistic Shielding for Safe Reinforcement Learning (AAAI 2025 · similarity 0.88)
- Long-Term Safe Reinforcement Learning with Binary Feedback (AAAI 2024 · similarity 0.87)
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