This chapter introduced the classical control tasks and the MuJoCo control tasks provided by Gym. You have learned the goals and specifications of these tasks and how to implement a simulator for them. The most important parts of this chapter were the deterministic DPG and the TRPO for continuous control tasks. You learned the theory behind them, which explains why they work well in these tasks. You also learned how to implement DPG and TRPO using TensorFlow, and how to visualize the training procedure.
In the next chapter, we will learn about how to apply reinforcement learning algorithms to more complex tasks, for example, playing Minecraft. We will introduce the Asynchronous Actor-Critic (A3C) algorithm, which is much faster than DQN at complex tasks, and has been widely applied as a framework in many deep reinforcement learning algorithms.
