  In model-based learning, an agent's model is commonly defined over
transitions between consecutive states of an environment even though planning
often requires reasoning over multi-step timescales, with intermediate states
either unnecessary, or worse, accumulating prediction error. In contrast,
intelligent behaviour in biological organisms is characterised by the ability
to plan over varying temporal scales depending on the context. Inspired by the
recent works on human time perception, we devise a novel approach to learning a
transition dynamics model, based on the sequences of episodic memories that
define the agent's subjective timescale - over which it learns world dynamics
and over which future planning is performed. We implement this in the framework
of active inference and demonstrate that the resulting subjective-timescale
model (STM) can systematically vary the temporal extent of its predictions
while preserving the same computational efficiency. Additionally, we show that
STM predictions are more likely to introduce future salient events (for example
new objects coming into view), incentivising exploration of new areas of the
environment. As a result, STM produces more informative action-conditioned
roll-outs that assist the agent in making better decisions. We validate
significant improvement in our STM agent's performance in the Animal-AI
environment against a baseline system, trained using the environment's
objective-timescale dynamics.
