Research map / Reinforcement learning / MDPs and planning
Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn CME with kernel least squares and projected weights instead of kernel smoothing | CME model-based policy iteration · cart-pole, mountain-car, quadrocopter | policy quality | Helped |
| compress the CME to a small compression set with principled L1 projection (vs full kernel least squares) | kernel least-squares CME · cart-pole, mountain-car | policy quality | Helped |
Closest papers by meaning
- Compressed Decentralized Learning of Conditional Mean Embedding Operators in Reproducing Kernel Hilbert Spaces (AAAI 2023 · similarity 0.88)
- Neural-Kernel Conditional Mean Embeddings (ICML 2024 · similarity 0.88)
- Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning (NeurIPS 2021 · similarity 0.88)
- Learning Markov State Abstractions for Deep Reinforcement Learning (NeurIPS 2021 · similarity 0.88)
- Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics (ICLR 2023 · similarity 0.87)
- Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs (NeurIPS 2024 · similarity 0.87)
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