Search Results for author:
Found 5 papers, 4 papers with code
SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies
We evaluate the effectiveness of our framework in diverse domains, including ADS, Atari Pong, Pacman and space-invaders games, using traditional performance metrics such as validity, proximity and sparsity.
Taming Transformers for Realistic Lidar Point Cloud Generation
Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the Lidar point cloud generation task, benefiting from their stable training and iterative refinement during sampling.
A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field
Anticipating the motion of other road users is crucial for automated driving systems (ADS), as it enables safe and informed downstream decision-making and motion planning.
SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement.
Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillation
Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems.
