4 citations · 12 across the 9 of their papers we have counts for
5 papers · 1 filter
Efficient Unsupervised Environment Design through Hierarchical Policy Representation Learning
Dexun Li, Sidney Tio, Pradeep Varakantham
Unsupervised Environment Design (UED) has emerged as a promising approach to developing general-purpose agents through automated curriculum generation. Popular UED methods focus on…
EduQate: Generating Adaptive Curricula through RMABs in Education Settings
Sidney Tio, Dexun Li, Pradeep Varakantham
There has been significant interest in the development of personalized and adaptive educational tools that cater to a student's individual learning progress. A crucial aspect in de…
Diversity Induced Environment Design via Self-Play
Dexun Li, Wenjun Li, Pradeep Varakantham
Recent work on designing an appropriate distribution of environments has shown promise for training effective generally capable agents. Its success is partly because of a form of a…
Generalization through Diversity: Improving Unsupervised Environment Design
Wenjun Li, Pradeep Varakantham, Dexun Li
Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e.g., moving in an 8x8 maze with three rooms, playing Ches…
CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks
Dexun Li, Meghna Lowalekar, Pradeep Varakantham
Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information. Recently, it has also found application in HIV…