8 citations · 25 across the 9 of their papers we have counts for
9 papers
Toward Enactive Artificial Intelligence
Banafsheh Rafiee, Richard Sutton
In this paper, we advocate for incorporating enactive approaches to perception and cognition into artificial intelligence (AI). Enactive approaches view perception as an active, sk…
The Minimax Rate of Perturbed Second-Order Calibration
Kamil Ciosek, Banafsheh Rafiee, Sina Ghiassian +1
Second-order calibration error quantifies how closely a higher-order predictor's epistemic-uncertainty estimate matches the conditional variance of the label probability on its lev…
Concise Reasoning via Reinforcement Learning
Mehdi Fatemi, Banafsheh Rafiee, Mingjie Tang +1
A major drawback of reasoning models is their excessive token usage, inflating computational cost, resource demand, and latency. We show this verbosity stems not from deeper reason…
Auxiliary task discovery through generate-and-test
Banafsheh Rafiee, Sina Ghiassian, Jun Jin +3
In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning. Auxiliary tasks tend to improve data effici…
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy
Banafsheh Rafiee, Jun Jin, Jun Luo +1
Auxiliary tasks have been argued to be useful for representation learning in reinforcement learning. Although many auxiliary tasks have been empirically shown to be effective for a…
From Eye-blinks to State Construction: Diagnostic Benchmarks for Online Representation Learning
Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian +4
We present three new diagnostic prediction problems inspired by classical-conditioning experiments to facilitate research in online prediction learning. Experiments in classical co…
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks
Sina Ghiassian, Banafsheh Rafiee, Yat Long Lo +1
Reinforcement learning systems require good representations to work well. For decades practical success in reinforcement learning was limited to small domains. Deep reinforcement l…
Two geometric input transformation methods for fast online reinforcement learning with neural nets
Sina Ghiassian, Huizhen Yu, Banafsheh Rafiee +1
We apply neural nets with ReLU gates in online reinforcement learning. Our goal is to train these networks in an incremental manner, without the computationally expensive experienc…
A First Empirical Study of Emphatic Temporal Difference Learning
Sina Ghiassian, Banafsheh Rafiee, Richard S. Sutton
In this paper we present the first empirical study of the emphatic temporal-difference learning algorithm (ETD), comparing it with conventional temporal-difference learning, in par…