81 citations · 160 across the 6 of their papers we have counts for
6 papers
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces
Ahmed Touati, Adrien Ali Taiga, Marc G. Bellemare
Despite the wealth of research into provably efficient reinforcement learning algorithms, most works focus on tabular representation and thus struggle to handle exponentially or in…
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment
Adrien Ali Taïga, William Fedus, Marlos C. Machado +2
This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses…
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc G. Bellemare, Will Dabney, Robert Dadashi +6
We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functions. We leverage this perspective to pro…
The Value Function Polytope in Reinforcement Learning
Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux +2
We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of…
Approximate Exploration through State Abstraction
Adrien Ali Taïga, Aaron Courville, Marc G. Bellemare
Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impractical. In this paper we study the interpla…
PixelVAE: A Latent Variable Model for Natural Images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed +4
Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but ha…