Research map / Reinforcement learning / Imitation learning
Inverse Decision Modeling: Learning Interpretable Representations of Behavior
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Recover Bayesian posterior of flexibility parameter alpha from simulated behavior with alpha_true=10^-5, 0.5, 10 (vs. ground truth) | Inverse bounded rational control (DIAG simulation) · DIAG simulated POMDP | recovered alpha vs true alpha | Helped |
| Model optimism (beta) and adaptivity (eta) jointly via inverse bounded rational control instead of standard Bayesian IRL reward learning | Inverse bounded rational control vs. conventional IRL · DIAG simulated POMDP, optimistic vs. non-adaptive behaviors | ability to differentiate two biased behaviors | Helped |
Closest papers by meaning
- Learning Utilities from Demonstrations in Markov Decision Processes (ICML 2025 · similarity 0.89)
- Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification (ICLR 2024 · similarity 0.88)
- Partial Identifiability in Inverse Reinforcement Learning for Agents with Non-Exponential Discounting (AAAI 2025 · similarity 0.87)
- Recent Advancements in Inverse Reinforcement Learning (AAAI 2024 · similarity 0.87)
- A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback (ICML 2024 · similarity 0.87)
- Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective (ICML 2024 · similarity 0.87)
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.
