cvar

The paper introduces quantile propagation planning (QPP), which propagates a fixed set of disturbance quantiles through dynamics to evaluate both mean and tail risk under a limited model-evaluation budget, showing reduced tail cost and constraint violations over mean-rollout planning on overload and inventory tasks.

This paper proposes a risk-averse online POMDP planning method that applies CVaR to the immediate cost over the belief at each step, keeping the standard expected cumulative return objective so existing expectation-based planners can be reused. The authors prove finite-time guarantees for policy evaluation, sparse sampling, and a bound on the gap between the particle belief MDP surrogate and the original POMDP, recovering standard planning in the risk-neutral limit.

This paper introduces Tail-Influence Sampling (TIS), a Neyman-style query allocation method for accurately estimating the lower-tail Conditional Value-at-Risk (CVaR) of fixed policies in stochastic workflows, achieving near-oracle variance and 2.4–3.4x lower MSE than rollouts on FinQA and outperforming rollout baselines on CliffWalking.

This paper proposes an adaptive training controller for CVaR risk-aware Q-learning, improving finite-budget behavior, reducing Bellman residuals by ~85%, and yielding better risk-adjusted performance in daily Bitcoin trading.

This paper proposes a deep reinforcement learning framework (MORP-DRL) for multi-objective reliability-based portfolio optimization, jointly optimizing expected return and downside risk using CVaR and EVaR under practical constraints, and demonstrates performance on global equity indices across different market regimes.