Exploration, Novelty, Surprise and Free Energy Minimisation

Abstract

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This paper reviews recent developments under the free energy principle that introduce a normative perspective on classical economic (utilitarian) decision-making based on (active) Bayesian inference. It has been suggested that the free energy principle precludes novelty and complexity, because it assumes that biological systems – like ourselves - try to minimise the long-term average of surprise to maintain their homeostasis. However, recent formulations show that minimising surprise leads naturally to concepts such as exploration and novelty bonuses. In this approach, agents infer a policy that minimises surprise by minimising the difference (or relative entropy) between likely and desired outcomes, which involves both pursuing the goal-state that has the highest expected utility (often termed ‘exploitation’) and visiting a number of different goal-states (‘exploration’). Crucially, the opportunity to visit new states increases the value of the current state. Casting decision-making problems within a variational framework, therefore, predicts that our behaviour is governed by both the entropy and expected utility of future states. This dissolves any dialectic between minimising surprise and exploration or novelty seeking.

Keywords

• reinforcement learning
• free energy
• active inference
• exploration
• exploitation
• novelty