Research map / Reinforcement learning / Policy gradient and actor-critic
A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use risk-aware entropic, mean-variance, or CVaR objectives instead of risk-neutral expected return | RAPTOR-SLP · Navigation | return variance | Helped |
| Use risk-aware entropic, mean-variance, or CVaR objectives instead of risk-neutral expected return | RAPTOR-SLP · Reservoir | worst-case return | Helped |
| Use CVaR planning instead of risk-neutral planning | RAPTOR-SLP · Reservoir | tail return | Helped |
| Use risk-aware entropic, mean-variance, or CVaR objectives instead of risk-neutral expected return | RAPTOR-SLP · HVAC | return variance | Helped |
| Account for risk in planning rather than optimizing expected return alone | RAPTOR-SLP · Navigation | expected cumulative reward | Mixed |
| Use CVaR planning instead of risk-neutral planning | RAPTOR-SLP · Navigation | expected cumulative reward | Helped |
| Use CVaR with small α | RAPTOR-SLP · HVAC | optimization stability | Hurt |
| Optimize a reactive policy rather than a straight-line plan | RAPTOR-DRP · Navigation, Reservoir, and HVAC | performance | Mixed |
Closest papers by meaning
- Beyond Expected Values: Risk-Sensitive Planning with Distributional Monte-Carlo Tree Search (NeurIPS 2026 · similarity 0.87)
- Planning and Learning in Average Risk-aware MDPs (NeurIPS 2025 · similarity 0.86)
- Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees (NeurIPS 2026 · similarity 0.86)
- Graph Sparse Sampling: Breaking the Curse of the Horizon in Continuous MDP Planning (NeurIPS 2026 · similarity 0.86)
- Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search (ICML 2025 · similarity 0.85)
- Risk-Conditioned Reinforcement Learning: A Generalized Approach for Adapting to Varying Risk Measures (AAAI 2024 · similarity 0.85)
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