REAL Reinforcement Learning: Planning with adversarial models

Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usually formalised as Markov Decision Processes, using a model of the environment dynamics to compute the optimal policy. When dealing with complex environments, the environment dynamics are frequently approximated with function approximators (such as...

Know what it does not know: Improving Offline Deep Reinforcement Learning with Uncertainty Estimation

Offline reinforcement learning, or learning from a fixed data set, is an attractive alternative to online reinforcement learning. Offline reinforcement learning promises to address the cost and safety implications of taking numerous random or bad actions online, which is a crucial aspect of traditional reinforcement learning that makes it...

Safe Semi-Supervised Learning

Semi-supervised algorithms have been shown to possibly have a worse performance than the corresponding supervised model. This may be due to a violation of the assumptions on the data that are introduced in most classification systems. We study an approach that was previously shown to have guarantees of improvement for the LDA classifier in terms...

Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification

Improvement guarantees for semi-supervised classifiers can currently only be given under restrictive conditions on the data. We propose a general way to perform semi-supervised parameter estimation for likelihood-based classifiers for which, on the full training set, the estimates are never worse than the supervised solution in terms of the log...