39 citations · 168 across the 10 of their papers we have counts for
5 papers · 1 filter
The Option Keyboard: Combining Skills in Reinforcement Learning
André Barreto, Diana Borsa, Shaobo Hou +8
The ability to combine known skills to create new ones may be crucial in the solution of complex reinforcement learning problems that unfold over extended periods. We argue that a…
Temporal Difference Uncertainties as a Signal for Exploration
Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann +7
An effective approach to exploration in reinforcement learning is to rely on an agent's uncertainty over the optimal policy, which can yield near-optimal exploration strategies in…
The Termination Critic
Anna Harutyunyan, Will Dabney, Diana Borsa +3
In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents. We propose an algorithm that focuses on th…
Learning Shared Representations in Multi-task Reinforcement Learning
Diana Borsa, Thore Graepel, John Shawe-Taylor
We investigate a paradigm in multi-task reinforcement learning (MT-RL) in which an agent is placed in an environment and needs to learn to perform a series of tasks, within this sp…
The Wreath Process: A totally generative model of geometric shape based on nested symmetries
Diana Borsa, Thore Graepel, Andrew Gordon
We consider the problem of modelling noisy but highly symmetric shapes that can be viewed as hierarchies of whole-part relationships in which higher level objects are composed of t…