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 …

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence
(AI) and represents a step toward building autonomous systems with a higher-level …

Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a
range of challenging sequential decision making and control tasks. However, these methods …

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step
towards building autonomous systems with a higher level understanding of the visual world …

Many potential applications of reinforcement learning (RL) require guarantees that the agent
will perform well in the face of disturbances to the dynamics or reward function. In this paper …

We propose a method for learning expressive energy-based policies for continuous states
and actions, which has been feasible only in tabular domains before. We apply our method …

We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy
Optimisation (MPO) based on coordinate ascent on a relative entropy objective. We show …

We establish a new connection between value and policy based reinforcement learning
(RL) based on a relationship between softmax temporal value consistency and policy …

This paper describes a path integral formulation of the free energy principle. The ensuing
account expresses the paths or trajectories that a particle takes as it evolves over time. The …

Safety is an essential component for deploying reinforcement learning (RL) algorithms in
real-world scenarios, and is critical during the learning process itself. A natural first approach …