Continuous Action Space
This chapter kicks off the advanced reinforcement learning (RL) part of the book by taking a look at a problem that has only been briefly mentioned: working with environments when our action space is not discrete. In this chapter, you will become familiar with the challenges that arise in such cases and learn how to solve them.
Continuous action space problems are an important subfield of RL, both theoretically and practically, because they have essential applications in robotics (which will be the subject of the next chapter), control problems, and other fields in which we communicate with physical objects.
In this chapter, we will:
- Cover the continuous action space, why it is important, how it differs from the already familiar discrete action space, and the way it is implemented in the Gym API
- Discuss the domain of continuous control using RL methods
- Check three different algorithms on the problem of a four-legged robot
