Research map / Reinforcement learning / MDPs and planning
A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a spherical-code ensemble of β-strictly separable IRL problems with Fano's inequality | Finite-state, finite-action IRL · Finite-state, finite-action IRL | sample complexity lower bound: O(n \log n) | Helped |
| Collect fewer than or close to the predicted lower-bound number of trajectories | IRL solution methods evaluated in simulation · Synthetic IRL, n=7 states and n=5 states | success rate: <50% | Hurt |
| Collect well above the predicted lower-bound number of trajectories | IRL solution methods evaluated in simulation · Synthetic IRL, n=7 states and n=5 states | success rate | Helped |
Closest papers by meaning
- How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach (NeurIPS 2024 · similarity 0.90)
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- Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective (ICML 2024 · similarity 0.87)
- Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs (NeurIPS 2024 · similarity 0.87)
- A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning (NeurIPS 2026 · similarity 0.87)
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