Domain-Independent Optimistic Initialization for Reinforcement Learning

Understand

In Reinforcement Learning (RL), it is common to use optimistic initialization of value functions to encourage exploration.

• However, such an approach generally depends on the domain, viz., the scale of the rewards must be known, and the feature representation must have a constant norm.
• We present a simple approach that performs optimistic initialization with less dependence on the domain.