Research map / Reinforcement learning / MDPs and planning
On the Correctness and Sample Complexity of Inverse Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the L1-regularized SVM formulation instead of the Ng & Russell linear programming formulation | Inverse reinforcement learning on randomly generated β-strictly separable MDPs · MDPs with n=5, k=5 and n=7, k=7 | success rate | Helped |
| Increase the number of transition samples | L1-regularized SVM for inverse reinforcement learning · Randomly generated β-strictly separable MDPs | success rate | Helped |
Closest papers by meaning
- A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning (ICML 2021 · similarity 0.92)
- Recent Advancements in Inverse Reinforcement Learning (AAAI 2024 · similarity 0.91)
- How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach (NeurIPS 2024 · similarity 0.90)
- Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective (ICML 2024 · similarity 0.90)
- Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces (NeurIPS 2024 · similarity 0.90)
- A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning (NeurIPS 2026 · similarity 0.90)
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