22 citations · 25 across the 2 of their papers we have counts for
4 papers
Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning
Pedro A. Tsividis, Joao Loula, Jake Burga +5
Reinforcement learning (RL) studies how an agent comes to achieve reward in an environment through interactions over time. Recent advances in machine RL have surpassed human expert…
Learning with AMIGo: Adversarially Motivated Intrinsic Goals
Andres Campero, Roberta Raileanu, Heinrich Küttler +3
A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new…
Logical Rule Induction and Theory Learning Using Neural Theorem Proving
Andres Campero, Aldo Pareja, Tim Klinger +2
A hallmark of human cognition is the ability to continually acquire and distill observations of the world into meaningful, predictive theories. In this paper we present a new mecha…
A First Step in Combining Cognitive Event Features and Natural Language Representations to Predict Emotions
Andres Campero, Bjarke Felbo, Joshua B. Tenenbaum +1
We explore the representational space of emotions by combining methods from different academic fields. Cognitive science has proposed appraisal theory as a view on human emotion wi…