Implementing the Deep Deterministic Policy Gradient algorithm and DDPG agent

Deterministic Policy Gradient (DPG) is a type of Actor-Critic RL algorithm that uses two neural networks: one for estimating the action value function, and the other for estimating the optimal target policy. The Deep Deterministic Policy Gradient (DDPG) agent builds upon the idea of DPG and is quite efficient compared to vanilla Actor-Critic agents due to the use of deterministic action policies. By completing this recipe, you will have access to a powerful agent that can be trained efficiently in a variety of RL environments.

Getting ready

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

import argparse
import os
import random
from collections import deque
from datetime import datetime
import gym
import numpy as np
import...