12 citations · 14 across the 5 of their papers we have counts for
4 papers · 1 filter
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…