
Entropic Risk Constrained SoftRobust Policy Optimization
Having a perfect model to compute the optimal policy is often infeasible...
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Partial Policy Iteration for L1Robust Markov Decision Processes
Robust Markov decision processes (MDPs) allow to compute reliable soluti...
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Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
In this paper, we introduce proximal gradient temporal difference learni...
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Optimizing NormBounded Weighted Ambiguity Sets for Robust MDPs
Optimal policies in Markov decision processes (MDPs) are very sensitive ...
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HighConfidence Policy Optimization: Reshaping Ambiguity Sets in Robust MDPs
Robust MDPs are a promising framework for computing robust policies in r...
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Robust Exploration with Tight Bayesian Plausibility Sets
Optimism about the poorly understood states and actions is the main driv...
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Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Robust MDPs (RMDPs) can be used to compute policies with provable worst...
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Tight Bayesian Ambiguity Sets for Robust MDPs
Robustness is important for sequential decision making in a stochastic d...
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Interpretable Reinforcement Learning with Ensemble Methods
We propose to use boosted regression trees as a way to compute humanint...
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A Practical Method for Solving Contextual Bandit Problems Using Decision Trees
Many efficient algorithms with strong theoretical guarantees have been p...
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Value Directed Exploration in MultiArmed Bandits with Structured Priors
Multiarmed bandits are a quintessential machine learning problem requir...
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Safe Policy Improvement by Minimizing Robust Baseline Regret
An important problem in sequential decisionmaking under uncertainty is ...
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Building an Interpretable Recommender via LossPreserving Transformation
We propose a method for building an interpretable recommender system for...
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Robust PartiallyCompressed LeastSquares
Randomized matrix compression techniques, such as the JohnsonLindenstra...
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A Bilinear Programming Approach for Multiagent Planning
Multiagent planning and coordination problems are common and known to be...
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Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation
We propose solution methods for previouslyunsolved constrained MDPs in ...
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An Approximate Solution Method for Large RiskAverse Markov Decision Processes
Stochastic domains often involve riskaverse decision makers. While rece...
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Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds
Approximate dynamic programming is a popular method for solving large Ma...
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Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large va...
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Marek Petrik
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