The idea of reusing or transferring information from previously learned tasks (source tasks)
for the learning of new tasks (target tasks) has the potential to significantly improve the …

Reinforcement Learning (RL) technologies are powerful to learn how to interact with
environments and have been successfully applied to various important applications. Q …

The computational search for the maximum-likelihood phylogenetic tree is an NP-hard
problem. As such, current tree search algorithms might result in a tree that is the local …

Abstract In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of tasks to
prepare for and learn faster in new, unseen, but related tasks. The training tasks are usually …

Although the notion of task similarity is potentially interesting in a wide range of areas such
as curriculum learning or automated planning, it has mostly been tied to transfer learning …

In many real-world applications, collecting and labeling the data is expensive and time-
consuming. Thus, there is a need to obtain a high-performance learner by leveraging the …

Reinforcement learning algorithms usually require a large number of empirical samples and
give rise to a slow convergence in practical applications. One solution is to introduce transfer …

In Meta-Reinforcement Learning (meta-RL) agents are trained on a set of tasks to prepare
for and learn faster in new, unseen, but related tasks. The standard practice to build training …

One of the many issues that has limited the usage of reinforcement learning (RL) in its
application to real-world problems, such as robotics, has been its inability to train agents to …

Although the notion of task similarity is potentially interesting in a wide range of areas such
as curriculum learning or automated planning, it has mostly been tied to transfer learning …