282 citations · 656 across the 14 of their papers we have counts for
6 papers · 1 filter
Solving Continuous Control via Q-learning
Tim Seyde, Peter Werner, Wilko Schwarting +4
While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the as…
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control Tasks
Ryan Sander, Wilko Schwarting, Tim Seyde +3
Experience replay plays a crucial role in improving the sample efficiency of deep reinforcement learning agents. Recent advances in experience replay propose using Mixup (Zhang et…
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Tim Seyde, Igor Gilitschenski, Wilko Schwarting +4
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space. In this work, we investigate the colloquially known…
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
Wilko Schwarting, Tim Seyde, Igor Gilitschenski +4
Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial interactions. This paper presents Deep Latent Competi…
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski +2
We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy. Our algorithm use…
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski +2
We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably app…