Research map / Reinforcement learning / Policy gradient and actor-critic
Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Control
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 |
|---|---|---|---|
| Add CPT probability weighting to utility-based policy optimization (ablation) | EUT-SPSA · Traffic signal control | CPT-value | Helped |
| Add utility functions and CPT probability weighting to mean-based policy optimization (ablation) | AVG-SPSA · Traffic signal control | CPT-value | Helped |
Closest papers by meaning
- Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning (ICML 2024 · similarity 0.88)
- Risk-Sensitive Reward-Free Reinforcement Learning with CVaR (ICML 2024 · similarity 0.88)
- Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation (ICML 2024 · similarity 0.88)
- A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents (ICML 2025 · similarity 0.88)
- Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback (ICLR 2024 · similarity 0.86)
- Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced Decision-Making in Distributional Reinforcement Learning (ICML 2025 · similarity 0.86)
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.