TensorFlow 2 Reinforcement Learning Cookbook

Chapter 3: Implementing Advanced RL Algorithms

This chapter provides short and crisp recipes to implement advanced Reinforcement Learning (RL) algorithms and agents from scratch using TensorFlow 2.x. It includes recipes to build Deep-Q-Networks (DQN), Double and Dueling Deep Q-Networks (DDQN, DDDQN), Deep Recurrent Q-Networks (DRQN), Asynchronous Advantage Actor-Critic (A3C), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradients (DDPG).

The following recipes are discussed in this chapter:

• Implementing the Deep Q-Learning algorithm, DQN, and Double-DQN agent
• Implementing the Dueling DQN agent
• Implementing the Dueling Double DQN algorithm and DDDQN agent
• Implementing the Deep Recurrent Q-Learning algorithm and DRQN agent ...