BCPPO: Bachelier-Inspired Constrained Proximal Policy Optimization for Tail-Risk-Aware Safe Reinforcement Learning
cs.LG, cs.AI
Submitted: 2026-08-31
Updated: 2026-08-31
Terminology
Sources
- Constrained Policy Optimization
- Constrained Policy Optimization via Bayesian World Models
- A Distributional Perspective on Reinforcement Learning
- SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy
- Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
- Implicit Quantile Networks for Distributional Reinforcement Learning
- Distributional Reinforcement Learning with Quantile Regression
- Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
- Safe Langevin Soft Actor Critic
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
- Constrained Variational Policy Optimization for Safe Reinforcement Learning
- High-Dimensional Continuous Control Using Generalized Advantage Estimation
- Proximal Policy Optimization Algorithms
- Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
- RACER: Epistemic Risk-Sensitive RL Enables Fast Driving with Fewer Crashes
- Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
- Policy Gradient for Coherent Risk Measures
- Optimizing the CVaR via Sampling
- Projection-Based Constrained Policy Optimization
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