Reinforcement learning is about learning agent models that make the best sequential
decisions in unknown environments. In an unknown environment, the agent needs to …

Covariance matrix adaptation evolution strategy (CMA-ES) is a successful gradient-free
optimization algorithm. Yet, it can hardly scale to handle high-dimensional problems. In this …

The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient derivative-free
optimization algorithm. It optimizes a black-box objective function over a well-defined …

Bayesian optimization (BayesOpt) is a derivative-free approach for sequentially optimizing
stochastic black-box functions. Standard BayesOpt, which has shown many successes in …

Reinforcement Learning (RL) problems appear in diverse real-world applications and are
gaining substantial attention in academia and industry. Policy Direct Search (PDS) is widely …

Algorithms usually consist of many hyperparameters that need to be tuned to perform
efficiently. It may be possible to tune a handful of parameters manually for simple algorithms …