Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …

Autonomously learning a complex task takes a very long time for Reinforcement Learning
(RL) agents. One way to learn faster is by dividing a complex task into several simple …

Abstract Knowledge transfer between tasks can improve the performance of learned models,
but requires an accurate estimate of inter-task relationships to identify the relevant …

Autonomous agents are increasingly required to solve complex tasks; hard-coding
behaviors has become infeasible. Hence, agents must learn how to solve tasks via …

Having the ability to solve increasingly complex problems using Reinforcement Learning
(RL) has prompted researchers to start develo** a greater interest in systematic …

Reinforcement learning (RL) is a widely known technique to enable autonomous learning.
Even though RL methods achieved successes in increasingly large and complex problems …

Curriculum value-based reinforcement learning (RL) solves a complex target task by reusing
action-values across a tailored sequence of related tasks of increasing difficulty. However …

As the Internet continues to get stronger, so does the potential risk of malicious users trying
to harm others. An intrusion detection system (IDS) can be used to alert the appropriate …

Learning to solve sequential decision-making tasks is difficult. Humans take years exploring
the environment essentially in a random way until they are able to reason, solve difficult …

Recently, there has been a focus on using transfer learning to reduce the sample complexity
in reinforcement learning. One component that enables transfer is an intertask map** that …