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Found 9 papers, 2 papers with code
Risk-Averse MDPs under Reward Ambiguity
We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity.
Policy Gradient in Robust MDPs with Global Convergence Guarantee
In contrast with prior robust policy gradient algorithms, DRPG monotonically reduces approximation errors to guarantee convergence to a globally optimal policy in tabular RMDPs.
Robust Phi-Divergence MDPs
In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty.
Fast Algorithms for $L_\infty$-constrained S-rectangular Robust MDPs
Robust Markov decision processes (RMDPs) are a useful building block of robust reinforcement learning algorithms but can be hard to solve.
Partial Policy Iteration for L1-Robust Markov Decision Processes
Robust Markov decision processes (MDPs) allow to compute reliable solutions for dynamic decision problems whose evolution is modeled by rewards and partially-known transition probabilities.
Optimizing Percentile Criterion Using Robust MDPs
We then propose new algorithms that minimize the span of ambiguity sets defined by weighted $L_1$ and $L_\infty$ norms.
Fast Bellman Updates for Robust MDPs
The first algorithm uses a homotopy continuation method to compute updates for L1-constrained s, a-rectangular ambiguity sets.
Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model
Myocardial Contrast Echocardiography (MCE) with micro-bubble contrast agent enables myocardial perfusion quantification which is invaluable for the early detection of coronary artery diseases.
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model
First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map.
