Publications (7)
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…
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…
Behaviour Suite for Reinforcement Learning
Ian Osband, Yotam Doron, Matteo Hessel +11
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabiliti…
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
Arsalan Sharifnassab, Saber Salehkaleybar, Richard Sutton
We address the challenge of optimizing meta-parameters (hyperparameters) in machine learning, a key factor for efficient training and high model performance. Rather than relying on…
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…
Step-size Optimization for Continual Learning
Thomas Degris, Khurram Javed, Arsalan Sharifnassab +2
In continual learning, a learner has to keep learning from the data over its whole life time. A key issue is to decide what knowledge to keep and what knowledge to let go. In a neu…
Toward Efficient Gradient-Based Value Estimation
Arsalan Sharifnassab, Richard Sutton
Gradient-based methods for value estimation in reinforcement learning have favorable stability properties, but they are typically much slower than Temporal Difference (TD) learning…