Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning (RL)
that learns from human feedback instead of relying on an engineered reward function. …

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial
optimisation problems for which numerous models and algorithms have been proposed. To …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake …

Social recommendation leverages social information to solve data sparsity and cold-start
problems in traditional collaborative filtering methods. However, most existing models assume …

Recently, deep reinforcement learning has shown promising results for learning fast heuristics
to solve routing problems. Meanwhile, most of the solvers suffer from generalizing to an …

As the operations of autonomous systems generally affect simultaneously several users, it is
crucial that their designs account for fairness considerations. In contrast to standard (deep) …

We propose a reinforcement learning approach to learning to teach. Following Torrey and
Taylor’s framework [18], an agent (the “teacher”) advises another one (the “student”) by …

We consider the problem of learning fair policies in (deep) cooperative multi-agent reinforcement
learning (MARL). We formalize it in a principled way as the problem of optimizing a …

We consider the problem of reliably selecting an optimal subset of fixed size from a given
set of choice alternatives, based on noisy information about the quality of these alternatives. …

We introduce a novel approach to preference-based reinforcement learning, namely a
preference-based variant of a direct policy search method based on evolutionary optimization. …