In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

This paper provides a concise description of the free energy principle, starting from a
formulation of random dynamical systems in terms of a Langevin equation and ending with a …

Since its introduction, the reward prediction error theory of dopamine has explained a wealth
of empirical phenomena, providing a unifying framework for understanding the …

According to the grounded perspective, cognition emerges from the interaction of classic
cognitive processes with the modalities, the body, and the environment. Rather than being …

The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …

Learning goal-directed behavior in environments with sparse feedback is a major challenge
for reinforcement learning algorithms. One of the key difficulties is insufficient exploration …

According to cognitive psychology and related disciplines, the development of complex
problem-solving behaviour in biological agents depends on hierarchical cognitive …

In recent years, deep learning models have demonstrated remarkable success in various
domains, such as computer vision, natural language processing, and speech recognition …

A large body of evidence has indicated that the phasic responses of midbrain dopamine
neurons show a remarkable similarity to a type of teaching signal (temporal difference (TD) …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …