This article surveys reinforcement learning approaches in social robotics. Reinforcement
learning is a framework for decision-making problems in which an agent interacts through …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …

Synthetic biology and bioengineering provide the opportunity to create novel embodied
cognitive systems (otherwise known as minds) in a very wide variety of chimeric …

In this paper we introduce a new unsupervised reinforcement learning method for
discovering the set of intrinsic options available to an agent. This set is learned by …

The mutual information is a core statistical quantity that has applications in all areas of
machine learning, whether this is in training of density models over multiple data modalities …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …

Exploration in complex domains is a key challenge in reinforcement learning, especially for
tasks with very sparse rewards. Recent successes in deep reinforcement learning have …

We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity
(SAGG-RIAC) architecture as an intrinsically motivated goal exploration mechanism which …

We present an approach for agents to learn representations of a global map from sensor
data, to aid their exploration in new environments. To achieve this, we embed procedures …