9 citations · 20 across the 6 of their papers we have counts for
6 papers
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…
Composing Efficient, Robust Tests for Policy Selection
Dustin Morrill, Thomas J. Walsh, Daniel Hernandez +2
Modern reinforcement learning systems produce many high-quality policies throughout the learning process. However, to choose which policy to actually deploy in the real world, they…
Event Tables for Efficient Experience Replay
Varun Kompella, Thomas J. Walsh, Samuel Barrett +2
Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems. However, uniform sampling from an ER buffer can lead to slow convergence and unstabl…
Batch-iFDD for Representation Expansion in Large MDPs
Alborz Geramifard, Thomas J. Walsh, Nicholas Roy +1
Matching pursuit (MP) methods are a promising class of feature construction algorithms for value function approximation. Yet existing MP methods require creating a pool of potentia…
Dynamic Teaching in Sequential Decision Making Environments
Thomas J. Walsh, Sergiu Goschin
We describe theoretical bounds and a practical algorithm for teaching a model by demonstration in a sequential decision making environment. Unlike previous efforts that have optimi…
Exploring compact reinforcement-learning representations with linear regression
Thomas J. Walsh, Istvan Szita, Carlos Diuk +1
This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm imp…