  We introduce a unified probabilistic framework for solving sequential
decision making problems ranging from Bayesian optimisation to contextual
bandits and reinforcement learning. This is accomplished by a probabilistic
model-based approach that explains observed data while capturing predictive
uncertainty during the decision making process. Crucially, this probabilistic
model is chosen to be a Meta-Learning system that allows learning from a
distribution of related problems, allowing data efficient adaptation to a
target task. As a suitable instantiation of this framework, we explore the use
of Neural processes due to statistical and computational desiderata. We apply
our framework to a broad range of problem domains, such as control problems,
recommender systems and adversarial attacks on RL agents, demonstrating an
efficient and general black-box learning approach.
