Policy Gradient Estimation

Policy gradient estimation is a method where an actor learns its behavior using a weighted policy function that maps parameters describing the current state to actions.

It is useful when the input parameters describing the environment state are continuous rather than categorical. The policy is learned without modeling the value function, i.e., without explicit consideration of the expected return from being in each state or carrying out each action. In practice, the policy is almost always modeled using a neural network. It uses gradient ascent to gradually learn the policy weights that maximize the overall return (‘the reward waiting at the end of the episode if this policy is followed’). Gradient ascent follows the same principle as the gradient descent used to minimize error in many other areas of machine learning.

For example, consider a robot learning to navigate a maze. The robot’s actions are determined by a policy modeled by a neural network. As the robot explores the maze, it updates its policy using gradient ascent to maximize the reward it receives for reaching the end of the maze.

In summary, policy gradient estimation is crucial for tasks requiring continuous action spaces and is a fundamental technique in reinforcement learning. It is important to know because it allows for direct optimization of policies in complex environments.