  This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm
as a formal account on the constructive interplay between boredom and curiosity
which gives rise to effective exploration and superior forward model learning.
We envisaged actions as instrumental in agent's own epistemic disclosure. This
motivated two central algorithmic ingredients: devaluation and devaluation
progress, both underpin agent's cognition concerning intrinsically generated
rewards. The two serve as an instantiation of homeostatic and heterostatic
intrinsic motivation. A key insight from our algorithm is that the two
seemingly opposite motivations can be reconciled---without which exploration
and information-gathering cannot be effectively carried out. We supported this
claim with empirical evidence, showing that boredom-enabled agents consistently
outperformed other curious or explorative agent variants in model building
benchmarks based on self-assisted experience accumulation.
