An agent learning an option in hierarchical reinforcement learning must solve three
problems: identify the option's subgoal (termination condition), learn a policy, and learn …

Due to the recent remarkable advances in artificial intelligence, researchers have begun to
consider challenging learning problems such as learning to generalize behavior from large …

While deep reinforcement learning (RL) agents outperform humans on an increasing
number of tasks, training them requires data equivalent to decades of human gameplay …

Intent-based network automation is a promising tool to enable easier network management
however certain challenges need to be effectively addressed. These are: 1) processing …

With the rise of computation power and machine learning techniques, a shift of research
interest is happening to roboticists. Against this background, this thesis seeks to develop or …

Autonomous intelligent agents often fail in complex dynamic multi-tasking scenarios that are
easily solvable for humans. For example, agents fail to adapt to new situations or cannot …