Search Results for author:
Found 7 papers, 6 papers with code
ARC: Adversarially Robust Control Policies for Autonomous Vehicles
By training the protagonist against an ensemble of adversaries, it learns a significantly more robust control policy, which generalises to a variety of adversarial strategies.
Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data
By penalising the safe action distribution based on its similarity to the unsafe action distribution when training on the collision dataset, a more robust and safe control policy is obtained.
Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments
Terrain traversability analysis plays a major role in ensuring safe robotic navigation in unstructured environments.
Self-adaptive Torque Vectoring Controller Using Reinforcement Learning
It is shown that, torque-vectoring controller with parameter tuning via reinforcement learning performs well on a range of different driving environment e. g., wide range of friction conditions and different velocities, which highlight the advantages of reinforcement learning as an adaptive algorithm for parameter tuning.
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance.
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
As the networks used to obtain state-of-the-art results become increasingly deep and complex, the rules they have learned and how they operate become more challenging to understand.
A Survey of Deep Learning Applications to Autonomous Vehicle Control
However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios.
