Practical reinforcement learning
Now that you have an intuitive understanding of what AI really means and the various classes of algorithm that drive its development, we will now focus on the practical aspects of building a reinforcement learning machine.
Here are the core concepts that you need to be aware of to develop reinforcement learning systems:
- Agent
- Rewards
- Environment
- State
- Value function
- Policy
Agent
In the reinforcement learning world, a machine is run or instructed by a (software) agent. The agent is the part of the machine that possesses intelligence and makes decisions on what to do next. You will come across the term "agent" several times as we dive deeper into reinforcement learning. Reinforcement learning is based on the reward hypothesis, which states that any goal can be described by the maximization of the expected cumulative reward. So, what is this reward exactly? That's what we'll discuss next.
Rewards
A reward, denoted by
, is usually a scalar quantity that is provided as...
