12 citations · 14 across the 5 of their papers we have counts for
7 papers
Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling
Aman Sinha, Payam Nikdel, Supratik Paul +1
Ensuring the safety of autonomous vehicles (AVs) requires both accurate estimation of their performance and efficient discovery of potential failure cases. This paper introduces Ba…
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula
Eli Bronstein, Sirish Srinivasan, Supratik Paul +4
ML-based motion planning is a promising approach to produce agents that exhibit complex behaviors, and automatically adapt to novel environments. In the context of autonomous drivi…
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
Eli Bronstein, Mark Palatucci, Dominik Notz +14
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving. We augment standard MGAIL…
Fast Efficient Hyperparameter Tuning for Policy Gradients
Supratik Paul, Vitaly Kurin, Shimon Whiteson
The performance of policy gradient methods is sensitive to hyperparameter settings that must be tuned for any new application. Widely used grid search methods for tuning hyperparam…
Learning from Demonstration in the Wild
Feryal Behbahani, Kyriacos Shiarlis, Xi Chen +8
Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical. It has succeeded in a wide range of problems but typically…
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Supratik Paul, Michael A. Osborne, Shimon Whiteson
Policy gradient methods ignore the potential value of adjusting environment variables: unobservable state features that are randomly determined by the environment in a physical set…
Alternating Optimisation and Quadrature for Robust Control
Supratik Paul, Konstantinos Chatzilygeroudis, Kamil Ciosek +3
Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators d…