265 citations · 489 across the 6 of their papers we have counts for
7 papers
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…
Meta-descent for Online, Continual Prediction
Andrew Jacobsen, Matthew Schlegel, Cameron Linke +3
This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be consid…
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study
Cam Linke, Nadia M. Ady, Martha White +2
Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the env…
The Predictron: End-To-End Learning and Planning
David Silver, Hado van Hasselt, Matteo Hessel +8
One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning. In this document we introduce the predictron architecture. Th…
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt +7
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, i…
Chi-square Tests Driven Method for Learning the Structure of Factored MDPs
Thomas Degris, Olivier Sigaud, Pierre-Henri Wuillemin
SDYNA is a general framework designed to address large stochastic reinforcement learning problems. Unlike previous model based methods in FMDPs, it incrementally learns the structu…
Off-Policy Actor-Critic
Thomas Degris, Martha White, Richard S. Sutton
This paper presents the first actor-critic algorithm for off-policy reinforcement learning. Our algorithm is online and incremental, and its per-time-step complexity scales linearl…