34 citations · 59 across the 14 of their papers we have counts for
7 papers · 1 filter
ABC: Adversarial Behavioral Cloning for Offline Mode-Seeking Imitation Learning
Eddy Hudson, Ishan Durugkar, Garrett Warnell +1
Given a dataset of expert agent interactions with an environment of interest, a viable method to extract an effective agent policy is to estimate the maximum likelihood policy indi…
DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation
Faraz Torabi, Garrett Warnell, Peter Stone
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the contr…
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy
Yunshu Du, Garrett Warnell, Assefaw Gebremedhin +2
Experience replay (ER) improves the data efficiency of off-policy reinforcement learning (RL) algorithms by allowing an agent to store and reuse its past experiences in a replay bu…
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…
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…
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration
Brahma S. Pavse, Faraz Torabi, Josiah P. Hanna +2
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch. However, most existing methods for integrating…