187 citations · 331 across the 16 of their papers we have counts for
6 papers · 1 filter
Coachable agents for interactive gameplay
Roberto Capobianco, Harm van Seijen, Nolan D. Bard +38
Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation m…
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Mingde Zhao, Safa Alver, Harm van Seijen +3
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situat…
Effective Multi-step Temporal-Difference Learning for Non-Linear Function Approximation
Harm van Seijen
Multi-step temporal-difference (TD) learning, where the update targets contain information from multiple time steps ahead, is one of the most popular forms of TD learning for linea…
True Online Temporal-Difference Learning
Harm van Seijen, A. Rupam Mahmood, Patrick M. Pilarski +2
The temporal-difference methods TD() and Sarsa() form a core part of modern reinforcement learning. Their appeal comes from their good performance, low computational cost, an…
An Empirical Evaluation of True Online TD(λ)
Harm van Seijen, A. Rupam Mahmood, Patrick M. Pilarski +1
The true online TD(λ) algorithm has recently been proposed (van Seijen and Sutton, 2014) as a universal replacement for the popular TD(λ) algorithm, in temporal-difference learning…
Planning by Prioritized Sweeping with Small Backups
Harm van Seijen, Richard S. Sutton
Efficient planning plays a crucial role in model-based reinforcement learning. Traditionally, the main planning operation is a full backup based on the current estimates of the suc…