104 citations · 224 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
Interactive Learning from Policy-Dependent Human Feedback
James MacGlashan, Mark K Ho, Robert Loftin +5
This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has…