154 citations · 1k across the 49 of their papers we have counts for
9 papers · 1 filter
Descriptive History Representations: Learning Representations by Answering Questions
Guy Tennenholtz, Jihwan Jeong, Chih-Wei Hsu +2
Effective decision making in partially observable environments requires compressing long interaction histories into informative representations. We introduce Descriptive History Re…
Factual and Personalized Recommendations using Language Models and Reinforcement Learning
Jihwan Jeong, Yinlam Chow, Guy Tennenholtz +4
Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditiona…
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Ofir Nachum, Yinlam Chow, Mohammad Ghavamzadeh
We study the sparse entropy-regularized reinforcement learning (ERL) problem in which the entropy term is a special form of the Tsallis entropy. The optimal policy of this formulat…
More Robust Doubly Robust Off-policy Evaluation
Mehrdad Farajtabar, Yinlam Chow, Mohammad Ghavamzadeh
We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another pol…
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson +1
In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i.e., increased awareness of events o…
Two Phase learning for Bidding-based Vehicle Sharing
Yinlam Chow, Jia Yuan Yu, Marco Pavone
We consider one-way vehicle sharing systems where customers can rent a car at one station and drop it off at another. The problem we address is to optimize the distribution of cars…
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
Yinlam Chow, Aviv Tamar, Shie Mannor +1
In this paper we address the problem of decision making within a Markov decision process (MDP) framework where risk and modeling errors are taken into account. Our approach is to m…
Policy Gradient for Coherent Risk Measures
Aviv Tamar, Yinlam Chow, Mohammad Ghavamzadeh +1
Several authors have recently developed risk-sensitive policy gradient methods that augment the standard expected cost minimization problem with a measure of variability in cost. T…
Algorithms for CVaR Optimization in MDPs
Yinlam Chow, Mohammad Ghavamzadeh
In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in costs in addition to minimizing a standard criterion. Conditiona…