50 citations · 74 across the 2 of their papers we have counts for
4 papers · 1 filter
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti +3
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over th…
RecSim: A Configurable Simulation Platform for Recommender Systems
Eugene Ie, Chih-wei Hsu, Martin Mladenov +5
We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim all…
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology
Eugene Ie, Vihan Jain, Jing Wang +10
Most practical recommender systems focus on estimating immediate user engagement without considering the long-term effects of recommendations on user behavior. Reinforcement learni…
Learning Curriculum Policies for Reinforcement Learning
Sanmit Narvekar, Peter Stone
Curriculum learning in reinforcement learning is a training methodology that seeks to speed up learning of a difficult target task, by first training on a series of simpler tasks a…