Step-size Optimization for Continual Learning

no code implementations • 30 Jan 2024 • Thomas Degris, Khurram Javed, Arsalan SharifNassab, Yuxin Liu, Richard Sutton

We conclude by suggesting that combining both approaches could be a promising future direction to improve the performance of neural networks in continual learning.

Continual Learning

Meta-descent for Online, Continual Prediction

no code implementations • 17 Jul 2019 • Andrew Jacobsen, Matthew Schlegel, Cameron Linke, Thomas Degris, Adam White, Martha White

This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems.

Second-order methods Time Series +1

Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study

no code implementations • 19 Jun 2019 • Cam Linke, Nadia M. Ady, Martha White, Thomas Degris, Adam White

The question we tackle in this paper is how to sculpt the stream of experience---how to adapt the learning system's behavior---to optimize the learning of a collection of value functions.

Active Learning reinforcement-learning +2

The Predictron: End-To-End Learning and Planning

1 code implementation • ICML 2017 • David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris

One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning.

Deep Reinforcement Learning in Large Discrete Action Spaces

2 code implementations • 24 Dec 2015 • Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Coppin

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.

Recommendation Systems reinforcement-learning +1

Deterministic Policy Gradient Algorithms

1 code implementation • International Conference on Machine Learning 2014 • David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, Martin Riedmiller

In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions.

reinforcement-learning Reinforcement Learning (RL)

Off-Policy Actor-Critic

1 code implementation • 22 May 2012 • Thomas Degris, Martha White, Richard S. Sutton

Previous work on actor-critic algorithms is limited to the on-policy setting and does not take advantage of the recent advances in off-policy gradient temporal-difference learning.

reinforcement-learning Reinforcement Learning (RL)