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
- Implementing the Asynchronous Advantage Actor-Critic algorithm and A3C agent
- Implementing the Proximal Policy Optimization algorithm and PPO agent
- Implementing the Deep Deterministic Policy Gradient algorithm and...
