33 citations · 53 across the 2 of their papers we have counts for
6 papers
Bayesian policy gradient and actor-critic algorithms
Mohammad Ghavamzadeh, Yaakov Engel, Michal Valko
Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Conventional policy gradient methods u…
Maximum Entropy Semi-Supervised Inverse Reinforcement Learning
Julien Audiffren, Michal Valko, Alessandro Lazaric +1
A popular approach to apprenticeship learning (AL) is to formulate it as an inverse reinforcement learning (IRL) problem. The MaxEnt-IRL algorithm successfully integrates the maxim…
Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)
Moonkyung Ryu, Chih-Wei Hsu, Yinlam Chow +2
While language models (LMs) offer great potential for conversational recommender systems (CRSs), the paucity of public CRS data makes fine-tuning LMs for CRSs challenging. In respo…
Ordering-based Conditions for Global Convergence of Policy Gradient Methods
Jincheng Mei, Bo Dai, Alekh Agarwal +3
We prove that, for finite-arm bandits with linear function approximation, the global convergence of policy gradient (PG) methods depends on inter-related properties between the pol…
Bayesian Regret Minimization in Offline Bandits
Marek Petrik, Guy Tennenholtz, Mohammad Ghavamzadeh
We study how to make decisions that minimize Bayesian regret in offline linear bandits. Prior work suggests that one must take actions with maximum lower confidence bound (LCB) on…
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes
Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh +1
Optimizing static risk-averse objectives in Markov decision processes is difficult because they do not admit standard dynamic programming equations common in Reinforcement Learning…