Hands-On Artificial Intelligence for IoT

Policy gradients

In the Q-learning-based methods, we generated a policy after estimating a value/Q-function. In policy-based methods, such as the policy gradient, we approximate the policy directly.

Continuing as earlier, here, we use a neural network to approximate the policy. In the simplest form, the neural network learns a policy for selecting the actions that maximize the rewards by adjusting its weights using steepest gradient ascent, hence the name policy gradients.

In policy gradients, the policy is represented by a neural network whose input is a representation of states and whose output is action selection probabilities. The weights of this network are the policy parameters that we need to learn. The natural question arises: how ...