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Reinforcement Learning can be described as a form of machine learning and is also a branch of Artificial Intelligence. It is able to allow machines to determine the ideal behavior in a specific context to maximize performance. In other words, it basically describes how software agents take actions in a specific environment to get some cumulative reward. Reward feedback is then sent to the agent which helps it learn its behavior. This is known as a reinforcement signal. Overall, the agent is learning this behavior based on the feedback its environment is giving it. The agent can then keep this behavior once learned or it can keep adapting based on its environment. It basically acts like a human. It learns through rewards. This is all done using many different algorithms.

Reinforcement learning itself is under a field called approximate dynamic programming. In AI learning, the environment is devised under Markov Decision Processes, or MDPs. These reinforcement learning algorithms utilize dynamic programming techniques. We aren't really going to dive into these algorithms today but just know that this is what reinforcement learning is operated on.

The good thing about Reinforcement Learning is that it is more efficient because it doesn't need to involve an expert on the domain of the application. This is because the agent learns its knowledge from direct inputs without domain heuristics and hard-engineered features. The process is already automated. Less time is spent designing a solution because the reinforcement learning for the machine is already in place.

Now, let's dive deeper into how an agent actually learns. The reinforcement learning model consists of:

  1. Series of agent and environments states S
  2. Series of actions A for actions
  3. Policies set in place when transitioning from states to actions
  4. Rules determining a reward of a transition
  5. Rules describing what the agents observe

Starting out, an agent will react with its environment in a unique time pattern. At each of these times, the agent will receive an observation and then will be followed by a reward. An action will then be chosen from the series of actions and it then interacts with the environment. After the interaction, the environment will proceed to move states and the reward that's associated with the overall transition will become established. Like I said earlier, the goal of the agent is to maximize long-term reward and performance. By going through all of these algorithms and processes, it learns which one maximizes performance and which one will provides the most reward. This one will be the most efficient for the agent.

Overall, Reinforcement learning is a very interesting topic. It can be summed up as an agent taking an action in an environment which gets interpreted into a reward. The reward and the representation of the state is then fed back into the agent. This is how the agent learns. It's a really cool process but also a really complex one at that. Hopefully we can see the benefits that reinforcement learning in the near future with AI technology.