104 citations · 224 across the 5 of their papers we have counts for
7 papers
Showing versus Doing: Teaching by Demonstration
Mark K. Ho, Michael L. Littman, James MacGlashan +2
People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration…
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…
Value Function Decomposition for Iterative Design of Reinforcement Learning Agents
James MacGlashan, Evan Archer, Alisa Devlic +4
Designing reinforcement learning (RL) agents is typically a difficult process that requires numerous design iterations. Learning can fail for a multitude of reasons, and standard R…
Gamma-Nets: Generalizing Value Estimation over Timescale
Craig Sherstan, Shibhansh Dohare, James MacGlashan +2
We present -nets, a method for generalizing value function estimation over timescale. By using the timescale as one of the estimator's inputs we can estimate value for arbitrary…
Implementing the Deep Q-Network
Melrose Roderick, James MacGlashan, Stefanie Tellex
The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research. However, replicating results for complex…
Environment-Independent Task Specifications via GLTL
Michael L. Littman, Ufuk Topcu, Jie Fu +3
We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent. The languag…