90 citations · 230 across the 45 of their papers we have counts for
10 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…
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning
Caroline Wang, Garrett Warnell, Peter Stone
While combining imitation learning (IL) and reinforcement learning (RL) is a promising way to address poor sample efficiency in autonomous behavior acquisition, methods that do so…
RAIL: A modular framework for Reinforcement-learning-based Adversarial Imitation Learning
Eddy Hudson, Garrett Warnell, Peter Stone
While Adversarial Imitation Learning (AIL) algorithms have recently led to state-of-the-art results on various imitation learning benchmarks, it is unclear as to what impact variou…
Skeletal Feature Compensation for Imitation Learning with Embodiment Mismatch
Eddy Hudson, Garrett Warnell, Faraz Torabi +1
Learning from demonstrations in the wild (e.g. YouTube videos) is a tantalizing goal in imitation learning. However, for this goal to be achieved, imitation learning algorithms mus…
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…
Generative Adversarial Imitation from Observation
Faraz Torabi, Garrett Warnell, Peter Stone
Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action informat…