153 citations · 385 across the 25 of their papers we have counts for
15 papers · 1 filter
Adversarial Intrinsic Motivation for Reinforcement Learning
Ishan Durugkar, Mauricio Tec, Scott Niekum +1
Learning with an objective to minimize the mismatch with a reference distribution has been shown to be useful for generative modeling and imitation learning. In this paper, we inve…
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…
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks
Lemeng Wu, Bo Liu, Peter Stone +1
We propose firefly neural architecture descent, a general framework for progressively and dynamically growing neural networks to jointly optimize the networks' parameters and archi…
Reinforcement Learning for Optimization of COVID-19 Mitigation policies
Varun Kompella, Roberto Capobianco, Stacy Jong +5
The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting…
Machine versus Human Attention in Deep Reinforcement Learning Tasks
Sihang Guo, Ruohan Zhang, Bo Liu +4
Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks. However, the trained models are often difficult to interpret, because they are…
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…