research
∙
06/02/2023
A Convex Relaxation Approach to Bayesian Regret Minimization in Offline Bandits
Algorithms for offline bandits must optimize decisions in uncertain envi...
share
research
∙
04/24/2023
On Dynamic Program Decompositions of Static Risk Measures
Optimizing static risk-averse objectives in Markov decision processes is...
research
∙
01/31/2023
Reducing Blackwell and Average Optimality to Discounted MDPs via the Blackwell Discount Factor
We introduce the Blackwell discount factor for Markov Decision Processes...
research
∙
12/20/2022
On the Convergence of Policy Gradient in Robust MDPs
Robust Markov decision processes (RMDPs) are promising models that provi...
research
∙
09/21/2022
On the convex formulations of robust Markov decision processes
Robust Markov decision processes (MDPs) are used for applications of dyn...
research
∙
09/09/2022
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk
Prior work on safe Reinforcement Learning (RL) has studied risk-aversion...
research
∙
05/27/2022
Robust Phi-Divergence MDPs
In recent years, robust Markov decision processes (MDPs) have emerged as...
research
∙
06/11/2021
Policy Gradient Bayesian Robust Optimization for Imitation Learning
The difficulty in specifying rewards for many real-world problems has le...
research
∙
01/04/2021
Robust Maximum Entropy Behavior Cloning
Imitation learning (IL) algorithms use expert demonstrations to learn a ...
research
∙
11/30/2020
Soft-Robust Algorithms for Handling Model Misspecification
In reinforcement learning, robust policies for high-stakes decision-maki...
research
∙
07/24/2020
Bayesian Robust Optimization for Imitation Learning
One of the main challenges in imitation learning is determining what act...
research
∙
06/20/2020
Entropic Risk Constrained Soft-Robust Policy Optimization
Having a perfect model to compute the optimal policy is often infeasible...
research
∙
06/16/2020
Partial Policy Iteration for L1-Robust Markov Decision Processes
Robust Markov decision processes (MDPs) allow to compute reliable soluti...
research
∙
06/06/2020
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity
In this paper, we introduce proximal gradient temporal difference learni...
research
∙
12/04/2019
Optimizing Norm-Bounded Weighted Ambiguity Sets for Robust MDPs
Optimal policies in Markov decision processes (MDPs) are very sensitive ...
research
∙
10/23/2019
High-Confidence Policy Optimization: Reshaping Ambiguity Sets in Robust MDPs
Robust MDPs are a promising framework for computing robust policies in r...
research
∙
04/17/2019
Robust Exploration with Tight Bayesian Plausibility Sets
Optimism about the poorly understood states and actions is the main driv...
research
∙
02/20/2019
Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs
Robust MDPs (RMDPs) can be used to compute policies with provable worst-...
research
∙
11/15/2018
Tight Bayesian Ambiguity Sets for Robust MDPs
Robustness is important for sequential decision making in a stochastic d...
research
∙
09/19/2018
Interpretable Reinforcement Learning with Ensemble Methods
We propose to use boosted regression trees as a way to compute human-int...
research
∙
06/14/2017
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees
Many efficient algorithms with strong theoretical guarantees have been p...
research
∙
04/12/2017
Value Directed Exploration in Multi-Armed Bandits with Structured Priors
Multi-armed bandits are a quintessential machine learning problem requir...
research
∙
07/13/2016
Safe Policy Improvement by Minimizing Robust Baseline Regret
An important problem in sequential decision-making under uncertainty is ...
research
∙
06/19/2016
Building an Interpretable Recommender via Loss-Preserving Transformation
We propose a method for building an interpretable recommender system for...
research
∙
10/16/2015
Robust Partially-Compressed Least-Squares
Randomized matrix compression techniques, such as the Johnson-Lindenstra...
research
∙
01/15/2014
A Bilinear Programming Approach for Multiagent Planning
Multiagent planning and coordination problems are common and known to be...
research
∙
09/26/2013
Solution Methods for Constrained Markov Decision Process with Continuous Probability Modulation
We propose solution methods for previously-unsolved constrained MDPs in ...
research
∙
10/16/2012
An Approximate Solution Method for Large Risk-Averse Markov Decision Processes
Stochastic domains often involve risk-averse decision makers. While rece...
research
∙
05/08/2012
Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds
Approximate dynamic programming is a popular method for solving large Ma...
research
∙
05/11/2010
