Policy Gradients
Policy Gradients are a class of reinforcement learning algorithms that directly optimize a policy function, which maps states to actions, by using gradient ascent on the expected cumulative reward. They work by estimating the gradient of the reward with respect to the policy parameters and updating the parameters to increase the likelihood of high-reward actions. This approach is model-free and can handle continuous action spaces, making it suitable for complex control tasks.
Developers should learn Policy Gradients when working on reinforcement learning problems where the action space is continuous or high-dimensional, such as robotics, autonomous driving, or game AI, as they can directly optimize stochastic policies without needing a value function. They are particularly useful in scenarios where exploration is critical, as they can learn probabilistic policies that balance exploration and exploitation. However, they often require careful tuning of hyperparameters and can suffer from high variance, so they are best applied with advanced variants like PPO or TRPO for stability.