Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge , the pixels-to-torques problem, where an RL agent learns a closed-loop control policy (" torques ") from pixel… (More)
We propose new hierarchical models and estimation techniques to solve the problem of heteroscedasticity in Bayesian optimisation. Our results demonstrate substantial gains in a wide range of applications, including automatic machine learning and mining exploration .
Multiple matching algorithms are used to locate the occurrences of patterns from a finite pattern set in a large input string. Aho-Corasick and Wu-Manber, two of the most well known algorithms for multiple matching require an increased computing power, particularly in cases where large-size datasets must be processed, as is common in computational biology… (More)