Policy Gradient Methods

Policy gradient methods are a class of reinforcement learning algorithms that optimize the agent's policy directly, rather than learning the value of states. By adjusting the probability of taking specific actions based on the rewards received, these methods allow for the handling of continuous action spaces common in derivatives trading.

This is particularly useful for tasks like dynamic position sizing or hedging, where the agent must determine the precise quantity of assets to buy or sell. Policy gradient methods are generally more stable in complex, high-dimensional environments compared to value-based methods.

They allow the agent to learn a smooth, responsive strategy that can adjust to rapid changes in market volatility and order flow.