activity
20162019
most citedSample-efficient Adversarial Imitation Learning from Observation

5 citations · 5 across the 2 of their papers we have counts for

collaborators

6 papers

cs.RO2019

Recent Advances in Imitation Learning from Observation

Faraz Torabi, Garrett Warnell, Peter Stone

Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that sa…

cs.LG2019

Imitation Learning from Video by Leveraging Proprioception

Faraz Torabi, Garrett Warnell, Peter Stone

Classically, imitation learning algorithms have been developed for idealized situations, e.g., the demonstrations are often required to be collected in the exact same environment a…

cs.LG20195 cited

Sample-efficient Adversarial Imitation Learning from Observation

Faraz Torabi, Sean Geiger, Garrett Warnell +1

Imitation from observation is the framework of learning tasks by observing demonstrated state-only trajectories. Recently, adversarial approaches have achieved significant performa…

cs.MM2019

Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning

Nicholas Waytowich, Sean L. Barton, Vernon Lawhern +2

While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniq…

cs.AI2017

Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces

Garrett Warnell, Nicholas Waytowich, Vernon Lawhern +1

While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot o…

stat.ML2016

Decentralized Dynamic Discriminative Dictionary Learning

Alec Koppel, Garrett Warnell, Ethan Stump +1

We consider discriminative dictionary learning in a distributed online setting, where a network of agents aims to learn a common set of dictionary elements of a feature space and m…