The recent successes of deep learning and deep reinforcement learning have firmly
established their statuses as state-of-the-art artificial learning techniques. However …

Exploration in reinforcement learning is, in general, a challenging problem. A common
technique to make learning easier is providing demonstrations from a human supervisor, but …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Applying reinforcement learning to combinatorial optimization problems is attractive as it
obviates the need for expert knowledge or pre-solved instances. However, it is unrealistic to …

There has been rapidly growing interest in meta-learning as a method for increasing the
flexibility and sample efficiency of reinforcement learning. One problem in this area of …

In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to
learn, improve, adapt and reproduce tasks with dynamically changing constraints based on …

Although creativity is studied from philosophy to cognitive robotics, a definition has proven
elusive. We argue for emphasizing the creative process (the cognition of the creative agent) …

Biological evolution has distilled the experiences of many learners into the general learning
algorithms of humans. Our novel meta reinforcement learning algorithm MetaGenRL is …

MushroomRL is an open-source Python library developed to simplify the process of
implementing and running Reinforcement Learning (RL) experiments. Compared to other …

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …