Implementing policy gradients

Policy gradient algorithms are fundamental to reinforcement learning and serve as the basis for several advanced RL algorithms. These algorithms directly optimize for the best policy, which can lead to faster learning compared to value-based algorithms. Policy gradient algorithms are effective for problems/applications with high-dimensional or continuous action spaces. This recipe will show you how to implement policy gradient algorithms using TensorFlow 2.0. Upon completing this recipe, you will be able to train an RL agent in any compatible OpenAI Gym environment.

Getting ready

To complete this recipe, you will need to activate the tf2rl-cookbook Python/conda virtual environment and run pip install -r requirements.txt. If the following import statements run without issues, you are ready to get started:

import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow import keras
from tensorflow.keras import layers
import numpy as...