Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x

This chapter provides a practical and concrete description of the fundamentals of Deep Reinforcement Learning (Deep RL) filled with recipes for implementing the building blocks using the latest major version of TensorFlow 2.x. It includes recipes for getting started with RL environments, OpenAI Gym, developing neural network-based agents, and evolutionary neural agents for addressing applications with both discrete and continuous value spaces for Deep RL.

The following recipes are discussed in this chapter:

  • Building an environment and reward mechanism for training RL agents
  • Implementing neural network-based RL policies for discrete action spaces and decision-making ...

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