Comparative advantages of actor-only versus critic-only RL paradigms in finance

Determine the comparative advantages of actor-only reinforcement learning methods (such as policy gradient) versus critic-only reinforcement learning methods (such as Q-learning) for financial portfolio optimization by conducting evaluations under consistent benchmarks and experimental setups that allow a rigorous comparison of their performance characteristics.

Background

Within model-free reinforcement learning applied to finance, algorithms are commonly categorized into actor-only (policy gradient) and critic-only (value-based, e.g., Q-learning) approaches. Actor-only methods directly optimize policies but can suffer from high variance and slow convergence in noisy market environments, while critic-only methods estimate value functions to provide stability but are less flexible in continuous action spaces.

The paper notes that inconsistent benchmarks and experimental setups across studies hinder clear conclusions about which paradigm is better suited for financial portfolio optimization. Establishing standardized evaluation protocols is necessary to clarify the comparative advantages of these two classes of methods.