Exploring through Random Curiosity with General Value Functions

Understand

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.