Quantifying uncertainty about a policy's long-term performance is import...
Bayesian optimization (BO) is a popular method to optimize costly black-...
We consider the problem of quantifying uncertainty over expected cumulat...
We consider a sequential decision making task where we are not allowed t...
Bayesian optimization is a powerful paradigm to optimize black-box funct...
Model-free reinforcement learning algorithms can compute policy gradient...
Model-based reinforcement learning algorithms with probabilistic dynamic...
Off-policy reinforcement learning algorithms promise to be applicable in...
In Interactive Machine Learning (IML), we iteratively make decisions and...
Gaussian processes are expressive, non-parametric statistical models tha...
Bayesian optimization (BO) based on Gaussian process models is a powerfu...
Efficient exploration remains a major challenge for reinforcement learni...
The energy output of photovoltaic (PV) power plants depends on the
envir...
Learning algorithms have shown considerable prowess in simulation by all...
Learning-based methods have been successful in solving complex control t...
Recent successes in reinforcement learning have lead to the development ...
Reinforcement learning is a powerful paradigm for learning optimal polic...
In classical reinforcement learning, when exploring an environment, agen...