224 citations · 308 across the 17 of their papers we have counts for
6 papers · 1 filter
Learning to Move Like Professional Counter-Strike Players
David Durst, Feng Xie, Vishnu Sarukkai +9
In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the co…
The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks
Iuri Frosio, Jan Kautz
Many defenses against adversarial attacks (\eg robust classifiers, randomization, or image purification) use countermeasures put to work only after the attack has been crafted. We…
Accelerating Reinforcement Learning through GPU Atari Emulation
Steven Dalton, Iuri Frosio, Michael Garland
We introduce CuLE (CUDA Learning Environment), a CUDA port of the Atari Learning Environment (ALE) which is used for the development of deep reinforcement algorithms. CuLE overcome…
Importance Estimation for Neural Network Pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree +2
Structural pruning of neural network parameters reduces computation, energy, and memory transfer costs during inference. We propose a novel method that estimates the contribution o…
Metaoptimization on a Distributed System for Deep Reinforcement Learning
Greg Heinrich, Iuri Frosio
Training intelligent agents through reinforcement learning is a notoriously unstable procedure. Massive parallelization on GPUs and distributed systems has been exploited to genera…
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree +2
We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming…