Before we deep dive into the details of reinforcement learning, I would like to cover some of the basics necessary for understanding the various nuts and bolts of RL methodologies. These basics appear across various sections of this chapter, which we will explain in detail whenever required:
- Environment: This is any system that has states, and mechanisms to transition between states. For example, the environment for a robot is the landscape or facility it operates.
- Agent: This is an automated system that interacts with the environment.
- State: The state of the environment or system is the set of variables or features that fully describe the environment.
- Goal or absorbing state or terminal state: This is the state that provides a higher discounted cumulative reward than any other state. A high cumulative reward prevents the best policy from being dependent on the initial state during training. Whenever an agent reaches its goal, we will finish one episode.
- Action:...