Policy Gradients

Policy gradients are a class of algorithms in reinforcement learning that optimize the policy directly. Instead of estimating the value function, these methods adjust the policy parameters based on the gradient of expected rewards. This approach allows for more flexible and effective learning in environments with high-dimensional action spaces. Common use cases include training agents in complex environments like games or robotics, where traditional methods may struggle to converge or generalize effectively.