16 citations · 20 across the 13 of their papers we have counts for
7 papers · 1 filter
Calibrated Partial Resets: Preventing Policy Collapse in Continual Reinforcement Learning
Luc McCutcheon, Evangelos Chatzaroulas, Saber Fallah
Neural networks are hindered by accumulating dormant neurons and loss of expressivity throughout training, particularly in non-stationary data settings, such as continual supervise…
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
Iman Sharifi, Mustafa Yildirim, Saber Fallah
Current imitation learning approaches, predominantly based on deep neural networks (DNNs), offer efficient mechanisms for learning driving policies from real-world datasets. Howeve…
ARC: Adversarially Robust Control Policies for Autonomous Vehicles
Sampo Kuutti, Saber Fallah, Richard Bowden
Deep neural networks have demonstrated their capability to learn control policies for a variety of tasks. However, these neural network-based policies have been shown to be suscept…
Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data
Sampo Kuutti, Saber Fallah, Richard Bowden
Imitation learning has been widely used to learn control policies for autonomous driving based on pre-recorded data. However, imitation learning based policies have been shown to b…
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
Sampo Kuutti, Richard Bowden, Saber Fallah
The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies prese…
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
Sampo Kuutti, Saber Fallah, Richard Bowden
Deep learning has become an increasingly common technique for various control problems, such as robotic arm manipulation, robot navigation, and autonomous vehicles. However, the do…