2 citations · 2 across the 1 of their papers we have counts for
7 papers
GrASP: Gradient-Based Affordance Selection for Planning
Vivek Veeriah, Zeyu Zheng, Richard Lewis +1
Planning with a learned model is arguably a key component of intelligence. There are several challenges in realizing such a component in large-scale reinforcement learning (RL) pro…
Accounting for Agreement Phenomena in Sentence Comprehension with Transformer Language Models: Effects of Similarity-based Interference on Surprisal and Attention
Soo Hyun Ryu, Richard L. Lewis
We advance a novel explanation of similarity-based interference effects in subject-verb and reflexive pronoun agreement processing, grounded in surprisal values computed from a pre…
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions
Ethan A. Brooks, Janarthanan Rajendran, Richard L. Lewis +1
Learning to flexibly follow task instructions in dynamic environments poses interesting challenges for reinforcement learning agents. We focus here on the problem of learning contr…
Learning State Representations from Random Deep Action-conditional Predictions
Zeyu Zheng, Vivek Veeriah, Risto Vuorio +2
Our main contribution in this work is an empirical finding that random General Value Functions (GVFs), i.e., deep action-conditional predictions -- random both in what feature of o…
How Should an Agent Practice?
Janarthanan Rajendran, Richard Lewis, Vivek Veeriah +2
We present a method for learning intrinsic reward functions to drive the learning of an agent during periods of practice in which extrinsic task rewards are not available. During p…
Discovery of Useful Questions as Auxiliary Tasks
Vivek Veeriah, Matteo Hessel, Zhongwen Xu +6
Arguably, intelligent agents ought to be able to discover their own questions so that in learning answers for them they learn unanticipated useful knowledge and skills; this depart…