Exploring through Random Curiosity with General Value Functions. (arXiv:2211.10282v1 [cs.LG])

Efficient exploration in reinforcement learning is a challenging problem
commonly addressed through intrinsic rewards. Recent prominent approaches are
based on state novelty or variants of artificial curiosity. However, directly
applying them to partially observable environments can be ineffective and lead
to premature dissipation of intrinsic rewards. Here we propose random curiosity
with general value functions (RC-GVF), a novel intrinsic reward function that
draws upon connections between these distinct approaches. Instead of using only
the current observation's novelty or a curiosity …

arxiv curiosity general random value