Domain-Independent Optimistic Initialization for Reinforcement Learning
This work addresses a specific issue in RL exploration for researchers, but appears incremental as it builds on existing optimistic initialization methods.
The paper tackled the problem of domain-dependent optimistic initialization in reinforcement learning by introducing a simpler approach that reduces this dependency, though no concrete results or numbers were provided.
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.