360 citations · 2.7k across the 150 of their papers we have counts for
18 papers · 1 filter
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
Kian Kenyon-Dean, Andre Cianflone, Lucas Page-Caccia +3
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representatio…
Environments for Lifelong Reinforcement Learning
Khimya Khetarpal, Shagun Sodhani, Sarath Chandar +1
To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific task but also to constantly build more com…
Off-Policy Deep Reinforcement Learning without Exploration
Scott Fujimoto, David Meger, Doina Precup
Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for…
The Barbados 2018 List of Open Issues in Continual Learning
Tom Schaul, Hado van Hasselt, Joseph Modayil +7
We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently act in complex environments. The purpose o…
Temporal Regularization in Markov Decision Process
Pierre Thodoroff, Audrey Durand, Joelle Pineau +1
Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensional domains. Regularization is a commonly…
Combined Reinforcement Learning via Abstract Representations
Vincent François-Lavet, Yoshua Bengio, Doina Precup +1
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly…