Reinforcement learning (RL) enables an agent to learn behavior by acquiring experience
through trial-and-error interactions with a dynamic environment. However, knowledge is …

We are interested in the following general question: is it possible to abstract knowledge that
is generated while learning the solution of a problem, so that this abstraction can accelerate …

Reinforcement learning enables a robot to learn behavior through trial-and-error. However,
knowledge is usually built from scratch and learning may take a long time. Many approaches …

In this paper we improve learning performance of a risk-aware robot facing navigation tasks
by employing transfer learning; that is, we use information from a previously solved task to …

In problems modeled as Markov Decision Processes (MDP), knowledge transfer is related to
the notion of generalization and state abstraction. Abstraction can be obtained through …

O aprendizado por reforço é uma técnica bem sucedida, porém lenta, para treinar agentes
autônomos. Algumas soluções baseadas em políticas parciais podem ser usadas para …

When designing intelligent agents that must solve sequential decision problems, often we
do not have enough knowledge to build a complete model for the problems at hand …