Research map / Reinforcement learning / MDPs and planning
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use KL-scaling and entropy-regularization annealing (ablation) | VAE-MDP training · CartPole, MountainCar, Acrobot, Pendulum, and LunarLander | posterior collapse | Helped |
| Add prioritized experience replay (ablation) | VAE-MDP training · CartPole, MountainCar, Acrobot, Pendulum, and LunarLander | posterior collapse | Helped |
| Maximize the VAE-MDP ELBO | VAE-MDP · CartPole, MountainCar, Acrobot, Pendulum, and LunarLander | local losses | Helped |
| Distill the RL policy into a latent policy | VAE-MDP · CartPole, MountainCar, Acrobot, Pendulum, and LunarLander | episode return | Helped |
Closest papers by meaning
- Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees (ICLR 2023 · similarity 0.93)
- Long-Term Safe Reinforcement Learning with Binary Feedback (AAAI 2024 · similarity 0.89)
- Oracle-Efficient Reinforcement Learning for Max Value Ensembles (NeurIPS 2024 · similarity 0.88)
- Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems (NeurIPS 2024 · similarity 0.88)
- Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds (ICLR 2025 · similarity 0.88)
- Near-Optimal Sample Complexity for Online Constrained MDPs (NeurIPS 2025 · similarity 0.88)
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