Extends robust MDPs to continuous time with policy gradient derivations using differential equation methods and proposes optimizers achieving linear convergence and specific sample complexities.
Operations Research , author=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Defines betweenness centrality in stochastic networks via absorbing Markov chain absorption times, estimated by Monte Carlo on random and real graphs.
RL training disrupts gradient-based adversarial attacks by inducing unstable low-magnitude gradients that limit the effectiveness of methods like PGD within practical budgets.
citing papers explorer
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Policy Gradient for Continuous-Time Robust Markov Decision Processes
Extends robust MDPs to continuous time with policy gradient derivations using differential equation methods and proposes optimizers achieving linear convergence and specific sample complexities.
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Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach
Defines betweenness centrality in stochastic networks via absorbing Markov chain absorption times, estimated by Monte Carlo on random and real graphs.
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Reinforcement Learning Disrupts Gradient-Based Adversarial Optimization
RL training disrupts gradient-based adversarial attacks by inducing unstable low-magnitude gradients that limit the effectiveness of methods like PGD within practical budgets.