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 …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

We study offline reinforcement learning (RL), which aims to learn an optimal policy based on
a dataset collected a priori. Due to the lack of further interactions with the environment …

Reinforcement learning allows solving complex tasks, however, the learning tends to be task-
specific and the sample efficiency remains a challenge. We present Plan2Explore, a self …

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex …

Understanding and interacting with everyday physical scenes requires rich knowledge
about the structure of the world, represented either implicitly in a value or policy function, or …

In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent
altogether. In such cases, curiosity can serve as an intrinsic reward signal to enable the …

Efficient exploration is a long-standing problem in sensorimotor learning. Major advances
have been demonstrated in noise-free, non-stochastic domains such as video games and …

How can artificial agents learn to solve many diverse tasks in complex visual environments
without any supervision? We decompose this question into two challenges: discovering new …

Count-based exploration algorithms are known to perform near-optimally when used in
conjunction with tabular reinforcement learning (RL) methods for solving small discrete …