Variational inference (VI) is a specific type of approximate Bayesian
in...
We introduce GPflux, a Python library for Bayesian deep learning with a
...
In the past decade, model-free reinforcement learning (RL) has provided
...
Gaussian processes (GPs) provide a framework for Bayesian inference that...
Cumulative entropy regularization introduces a regulatory signal to the
...
Empowerment is an information-theoretic method that can be used to
intri...
Though successful in high-dimensional domains, deep reinforcement learni...
Inspired by findings of sensorimotor coupling in humans and animals, the...
Within the context of video games the notion of perfectly rational agent...
In this paper, we methodologically address the problem of cumulative rew...
Reinforcement learning is concerned with identifying reward-maximizing
b...
Information-theoretic principles for learning and acting have been propo...
Bounded rational decision-makers transform sensory input into motor outp...