Teaching Agents to Explore When Rewards Are Rare
A simulation study tests whether a simple curiosity bonus can help reinforcement learning agents find rewards that appear only once in a long while.
In one paragraph
A peer-reviewed simulation study compares a count-based curiosity bonus against standard exploration in 40 sparse-reward environments. The bonus helped in most of them, but the gains were uneven and depended on how states were counted. The authors report careful controls across many random seeds. The results come from simulated tasks, so real-world transfer is untested.
Reinforcement learning AI & Machine Learning
Briefing reviewed by an editor before publication. The paper belongs to its authors; this page summarises it and links to the original. Reviewed by Dr. C. Mbeki. Paper licence: CC BY 4.0. Corrections: support@papersays.com.