182 citations · 924 across the 64 of their papers we have counts for
65 papers
Value-aware Importance Weighting for Off-policy Reinforcement Learning
Kristopher De Asis, Eric Graves, Richard S. Sutton
Importance sampling is a central idea underlying off-policy prediction in reinforcement learning. It provides a strategy for re-weighting samples from a distribution to obtain unbi…
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks
Khurram Javed, Haseeb Shah, Rich Sutton +1
Constructing states from sequences of observations is an important component of reinforcement learning agents. One solution for state construction is to use recurrent neural networ…
Toward Efficient Gradient-Based Value Estimation
Arsalan Sharifnassab, Richard Sutton
Gradient-based methods for value estimation in reinforcement learning have favorable stability properties, but they are typically much slower than Temporal Difference (TD) learning…
Auxiliary task discovery through generate-and-test
Banafsheh Rafiee, Sina Ghiassian, Jun Jin +3
In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning. Auxiliary tasks tend to improve data effici…
On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly Communicating MDPs
Yi Wan, Richard S. Sutton
We show two average-reward off-policy control algorithms, Differential Q-learning (Wan, Naik, & Sutton 2021a) and RVI Q-learning (Abounadi Bertsekas & Borkar 2001), converge in wea…
The Alberta Plan for AI Research
Richard S. Sutton, Michael Bowling, Patrick M. Pilarski
Herein we describe our approach to artificial intelligence research, which we call the Alberta Plan. The Alberta Plan is pursued within our research groups in Alberta and by others…