activity
20162023
most citedHuman-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning

22 citations · 30 across the 3 of their papers we have counts for

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Showing cs.LGShow all

2 papers · 1 filter

cs.LG2023★ 4 cited

Successor-Predecessor Intrinsic Exploration

Changmin Yu, Neil Burgess, Maneesh Sahani +1

Exploration is essential in reinforcement learning, particularly in environments where external rewards are sparse. Here we focus on exploration with intrinsic rewards, where the a…

cs.LG2020

Language-Mediated, Object-Centric Representation Learning

Ruocheng Wang, Jiayuan Mao, Samuel J. Gershman +1

We present Language-mediated, Object-centric Representation Learning (LORL), a paradigm for learning disentangled, object-centric scene representations from vision and language. LO…