Many subfields of machine learning share a common stumbling block: evaluation. Advances
in machine learning often evaporate under closer scrutiny or turn out to be less widely …

There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones,
in different applications such as packages delivery, traffic monitoring, search and rescue …

Developing a general algorithm that learns to solve tasks across a wide range of
applications has been a fundamental challenge in artificial intelligence. Although current …

Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a
promising path towards sustainable energy. A core challenge is to shape and maintain a …

Imitating human demonstrations is a promising approach to endow robots with various
manipulation capabilities. While recent advances have been made in imitation learning and …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Abstract We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework
which modifies traditional on-policy actor-critic methods by separating policy and value …

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used
evaluation tasks and criteria, making comparisons between approaches difficult. In this work …

Reward hacking--where RL agents exploit gaps in misspecified reward functions--has been
widely observed, but not yet systematically studied. To understand how reward hacking …

Standard deep reinforcement learning algorithms use a shared representation for the policy
and value function, especially when training directly from images. However, we argue that …