Research map / Reinforcement learning / Imitation learning
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Gives the first provably sample-efficient offline and online algorithms for tabular IRL using a pessimism-based reward-mapping formulation and a stronger performance metric, with near-matching lower bounds.
Problem
Inverse reinforcement learning in standard offline (pre-collected data) and online settings lacks efficient algorithms and a commonly agreed metric for comparing estimated and true feasible reward sets.
Approach
Characterize the feasible reward set through a reward mapping parameterized by value and advantage functions, define a stronger mapping-based metric on visitation-averaged value differences, and adapt offline RL pessimism (Bernstein-style penalty on empirical transitions and expert policy support) to guarantee monotonicity and small metric error; online setting reduces to reward-free exploration p
Key results
- Offline algorithm RLP achieves epsilon error in the mapping metric with K = Õ(H^4 S C* log N / ε^2 + H^2 S C* η / ε) episodes, with H^3 improvement when π^eval = π^E.
- Offline lower bound Ω(H^2 S C* min{S,A}/ε^2) shows the upper bound is nearly tight for S ≤ A.
- Online algorithm RLE achieves Õ(H^4 SA log N/ε^2) episodes under the stronger D^all metric, with a matching lower bound Ω(H^3 SA min{S,A}/ε^2) up to H and log factors.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use offline RLP with pessimism and the single-policy concentrability assumption | Offline IRL · Offline IRL, evaluation policy | episodes required for D^pi evaluation error at most epsilon: K \ge \widetilde{\cO}\paren{\frac{H^4SC^\star \cns}{\epsilon^2}+\frac{H^2SC^\star \eta}{\epsilon}} | Mixed |
| Evaluate the learned reward mapping using the expert policy, under the stated bounded-reward condition | RLP · Offline IRL, evaluation policy equals expert policy | episodes required for D^pi evaluation error at most epsilon: H^4\to H^3 | Helped |
| Set the behavior policy equal to the expert policy | RLP · Offline IRL, full expert trajectories | episodes required: \widetilde{\cO}(H^3S\cns/\epsilon^2) | Helped |
| Use active exploration via reward-free exploration before applying RLP | Online IRL · Online IRL | episodes required for D^all error at most epsilon: K + NH \ge \widetilde{\cO}\paren{\frac{H^4SA \cns}{\epsilon^2}+\frac{H^2SA \eta}{\epsilon}} | Helped |
| Use the D^L performance metric instead of D^all_Theta | RLE · Online IRL | episodes required: \widetilde{\cO}\paren{\frac{H^4S^2A}{\epsilon^2}+\frac{H^2SA\eta}{\epsilon}} | Mixed |
Surprising findings
- The commonly used D^L metric from prior online IRL work fails to distinguish reward mappings that are far apart under the proposed metric.
- Under the well-posedness assumption the expert policy's support need not be assumed when the evaluation policy equals the expert policy with concentrability.
Limitations
- Results are for tabular MDPs; generalization to function approximation is left open.
- Lower bound has a min{S,A} factor instead of S in general, so tightness holds only for S ≤ A.
- No empirical experiments are reported.
Experimental setup
Metrics: sample complexity D^{all}_Theta mapping metric Hausdorff distance
Baselines: simulator-based IRL (Metelli et al.) online IRL of Xu et al. (D^L metric) Hausdorff-based IRL analyses
Builds on: pessimism in offline RL reward-free exploration Bernstein-style penalty
Closest papers by meaning
- Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms (ICML 2024 · similarity 0.95)
- Recent Advancements in Inverse Reinforcement Learning (AAAI 2024 · similarity 0.95)
- Hybrid Inverse Reinforcement Learning (ICML 2024 · similarity 0.93)
- How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach (NeurIPS 2024 · similarity 0.92)
- Inverse Reinforcement Learning by Estimating Expertise of Demonstrators (AAAI 2025 · similarity 0.92)
- Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching (ICLR 2025 · similarity 0.91)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.