Policy Gradient Methods
Policy Gradient Methods are a class of reinforcement learning algorithms that directly optimize the policy function, which maps states to actions, by adjusting its parameters to maximize expected cumulative reward. They work by estimating the gradient of the expected reward with respect to the policy parameters and using gradient ascent to update the policy. These methods are model-free and can handle continuous action spaces, making them suitable for complex control problems.
Developers should learn Policy Gradient Methods when working on reinforcement learning tasks that require handling high-dimensional or continuous action spaces, such as robotics, game AI, or autonomous systems. They are particularly useful when the environment dynamics are unknown or too complex to model, as they directly learn a policy without needing a value function or model. Use cases include training agents in simulated environments like OpenAI Gym or real-world applications where traditional Q-learning methods struggle with continuous actions.