153 citations · 860 across the 45 of their papers we have counts for
8 papers · 1 filter
Conditional Importance Sampling for Off-Policy Learning
Mark Rowland, Anna Harutyunyan, Hado van Hasselt +4
The principal contribution of this paper is a conceptual framework for off-policy reinforcement learning, based on conditional expectations of importance sampling ratios. This fram…
Adaptive Trade-Offs in Off-Policy Learning
Mark Rowland, Will Dabney, Rémi Munos
A great variety of off-policy learning algorithms exist in the literature, and new breakthroughs in this area continue to be made, improving theoretical understanding and yielding…
A Generalized Training Approach for Multiagent Learning
Paul Muller, Shayegan Omidshafiei, Mark Rowland +12
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense that it (1)…
Multiagent Evaluation under Incomplete Information
Mark Rowland, Shayegan Omidshafiei, Karl Tuyls +4
This paper investigates the evaluation of learned multiagent strategies in the incomplete information setting, which plays a critical role in ranking and training of agents. Tradit…
Meta-learning of Sequential Strategies
Pedro A. Ortega, Jane X. Wang, Mark Rowland +21
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class. O…
Orthogonal Estimation of Wasserstein Distances
Mark Rowland, Jiri Hron, Yunhao Tang +3
Wasserstein distances are increasingly used in a wide variety of applications in machine learning. Sliced Wasserstein distances form an important subclass which may be estimated ef…