424 citations · 2.2k across the 42 of their papers we have counts for
43 papers
Off-policy Distributional Q(): Distributional RL without Importance Sampling
Yunhao Tang, Mark Rowland, Rémi Munos +2
We introduce off-policy distributional Q(), a new addition to the family of off-policy distributional evaluation algorithms. Off-policy distributional Q() does not apply impo…
A Distributional Analogue to the Successor Representation
Harley Wiltzer, Jesse Farebrother, Arthur Gretton +5
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Anal…
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
Mark Rowland, Li Kevin Wenliang, Rémi Munos +3
We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative…
Bootstrapped Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Mark Rowland +4
In reinforcement learning (RL), state representations are key to dealing with large or continuous state spaces. While one of the promises of deep learning algorithms is to automati…
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
Mark Rowland, Yunhao Tang, Clare Lyle +3
We study the problem of temporal-difference-based policy evaluation in reinforcement learning. In particular, we analyse the use of a distributional reinforcement learning algorith…
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo +4
Representation learning and exploration are among the key challenges for any deep reinforcement learning agent. In this work, we provide a singular value decomposition based method…