121 citations · 591 across the 29 of their papers we have counts for
5 papers · 1 filter
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning
Tongzheng Ren, Tianjun Zhang, Csaba Szepesvári +1
Representation learning lies at the heart of the empirical success of deep learning for dealing with the curse of dimensionality. However, the power of representation learning has…
C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov +2
Goal-conditioned reinforcement learning (RL) can solve tasks in a wide range of domains, including navigation and manipulation, but learning to reach distant goals remains a centra…
Multi-objective Optimization by Learning Space Partitions
Yiyang Zhao, Linnan Wang, Kevin Yang +3
In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are no…
MADE: Exploration via Maximizing Deviation from Explored Regions
Tianjun Zhang, Paria Rashidinejad, Jiantao Jiao +3
In online reinforcement learning (RL), efficient exploration remains particularly challenging in high-dimensional environments with sparse rewards. In low-dimensional environments,…
Learning Space Partitions for Path Planning
Kevin Yang, Tianjun Zhang, Chris Cummins +6
Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function. Popular approaches like…