Policy Gradients

Policy gradients are a reinforcement learning method where an agent learns to make decisions by directly adjusting a policy—its way of choosing actions—based on feedback from its environment. The agent tries actions, observes the results, and then updates its decision-making process to favor actions that lead to better outcomes. By calculating the gradient (or direction) to improve its policy, the agent iteratively refines its behavior, improving performance over time. This approach is especially useful for complex or continuous action spaces where other methods may struggle, enabling more efficient and flexible learning.