IJCAI 2017: Contextual CMA-ES

IJCAI 2017: Contextual CMA-ES

Many stochastic search algorithms are designed to optimize a fixed objective function to learn a task, i.e., if the objective function changes slightly, for example,…

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ICML 2017: Local Bayesian Optimization

ICML 2017: Local Bayesian Optimization

Bayesian optimization is renowned for its sample efficiency but its application to higher dimensional tasks is impeded by its focus on global optimization. To scale to higher dimensional problems,…

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GECCO 2017: Deriving and Improving CMA-ES with Information-Geometric Trustregions

GECCO 2017: Deriving and Improving CMA-ES with Information-Geometric Trustregions

CMA-ES is one of the most popular stochastic search algorithms. It performs favourably in many tasks without the need of extensive parameter tuning. The algorithm…

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ICRA 2017: Empowered Skills

ICRA 2017: Empowered Skills

Robot Reinforcement Learning (RL) algorithms return a policy that maximizes a global cumulative reward signal but typically do not create diverse behaviors. Hence, the policy…

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ICRA 2017: Layered Direct Policy Search for Learning Hierarchical Skills

ICRA 2017: Layered Direct Policy Search for Learning Hierarchical Skills

Solutions to real world robotic tasks often require complex behaviors in high dimensional continuous state and action spaces. Reinforcement Learning (RL) is aimed at learning…

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NIPS 2015: Model-Based Relative Entropy Stochastic Search

NIPS 2015: Model-Based Relative Entropy Stochastic Search

Stochastic search algorithms are general black-box optimizers. Due to their ease of use and their generality, they have recently also gained a lot of attention…

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