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Found 5 papers, 2 papers with code
On-Policy Model Errors in Reinforcement Learning
In contrast, model-based methods can use the learned model to generate new data, but model errors and bias can render learning unstable or suboptimal.
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework.
Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection
Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design and parameter optimization.
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Second, we propose a linearization-based technique that directly provides approximations of the trajectory distribution, taking correlations explicitly into account.
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization.
