7 citations · 7 across the 13 of their papers we have counts for
13 papers
Adaptive Reconstruction of Bosonic Quantum States
Vasilisa Usova, Phila Rembold, Ian Yang +4
Bosonic quantum systems provide a hardware-efficient platform for quantum information processing but remain challenging to characterise due to their large Hilbert space and the hig…
Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks
Muthukumar Pandaram, Jakob Hollenstein, David Drexel +3
The use of learned dynamics models, also known as world models, can improve the sample efficiency of reinforcement learning. Recent work suggests that the underlying causal graphs…
Investigating the Benefits of Nonlinear Action Maps in Data-Driven Teleoperation
Michael Przystupa, Gauthier Gidel, Matthew E. Taylor +3
As robots become more common for both able-bodied individuals and those living with a disability, it is increasingly important that lay people be able to drive multi-degree-of-free…
Deep Probabilistic Movement Primitives with a Bayesian Aggregator
Michael Przystupa, Faezeh Haghverd, Martin Jagersand +1
Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models tha…
Dynamic Decision Frequency with Continuous Options
Amirmohammad Karimi, Jun Jin, Jun Luo +3
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as settin…
A Temporal-Difference Approach to Policy Gradient Estimation
Samuele Tosatto, Andrew Patterson, Martha White +1
The policy gradient theorem (Sutton et al., 2000) prescribes the usage of a cumulative discounted state distribution under the target policy to approximate the gradient. Most algor…
Quintessential inflation and nonlinear effects of the tachyonic trap mechanism
Mindaugas Karčiauskas, Stanislav Rusak, Alejandro Saez
With the help of the tachyonic trapping mechanism one can potentially solve a number of problems affecting quintessential inflation models. In this mechanism we introduce a trappin…
Model-free Policy Learning with Reward Gradients
Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi +1
Despite the increasing popularity of policy gradient methods, they are yet to be widely utilized in sample-scarce applications, such as robotics. The sample efficiency could be imp…
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient
Samuele Tosatto, João Carvalho, Jan Peters
Off-policy Reinforcement Learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current…
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills
Samuele Tosatto, Georgia Chalvatzaki, Jan Peters
Parameterized movement primitives have been extensively used for imitation learning of robotic tasks. However, the high-dimensionality of the parameter space hinders the improvemen…
Dimensionality Reduction of Movement Primitives in Parameter Space
Samuele Tosatto, Jonas Stadtmueller, Jan Peters
Movement primitives are an important policy class for real-world robotics. However, the high dimensionality of their parametrization makes the policy optimization expensive both in…
An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
Samuele Tosatto, Riad Akrour, Jan Peters
The Nadaraya-Watson kernel estimator is among the most popular nonparameteric regression technique thanks to its simplicity. Its asymptotic bias has been studied by Rosenblatt in 1…
A Nonparametric Off-Policy Policy Gradient
Samuele Tosatto, Joao Carvalho, Hany Abdulsamad +1
Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is es…