Reinforcement Learning Algorithms with Python

The gradient of the policy

The objective of RL is to maximize the expected return (the total reward, discounted or undiscounted) of a trajectory. The objective function, can then be expressed as:

Where θ is the parameters of the policy, such as the trainable variables of a deep neural network.

In PG methods, the maximization of the objective function is done through the gradient of the objective function . Using gradient ascent, we can improve by moving the parameters toward the direction of the gradient, as the gradient points in the direction ...