Reinforcement Machine Learning

We will discuss Reinforcement Learning. Reinforcement means to establish or encourage a pattern of behavior. Let us say that you were dropped off at an isolated island. What would you do? Now initially you would panic and you would be unsure of what to do where to get food from how to live and so on. But after a while you will have to adapt you must learn how to live on the island. Adapt to the changing climates learn more to eat and what not to eat.

So here you are basically following the hit and trial concept because you are new to the surrounding and the only way to learn is experience and then learn from your experience. This is what reinforcement learning is. It is a learning method wherein an agent, which is basically you stuck on the island interacts with its environment which is the island by producing actions and discovers errors or rewards. Once the agent gets trained it gets ready to predict the new data presented to it.

Definition

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Reinforcement Learning is a Machine Learning approach. The basic concept behind reinforcement learning is that there is an agent now this agent is put in an unknown environment. So, the agent has to explore the environment by taking actions and transitioning from one state to the other so that he can get maximum rewards.

Types of Problems

In reinforcement learning, the key difference is that the input itself depends on the actions we take. For example, in robotics? We might start in a situation where the robot does not know anything above the surrounding it is in. So, after it performs certain actions, it finds out more about the world. But the world it sees depends on whether it chooses to move right or whether it shows to move forward or backward. In this case, the robot is known as the agent and its surrounding is the environment. So, for each action, it takes it can receive a reward or it might receive a punishment.

Type of Data

In reinforcement learning, there is no predefined data the input depends on the actions taken by the agent. Now, these actions are then recorded in the form of matrices so that they can serve as a memory to the agent. So basically, as the agent explores the environment, it will collect data, which was then being used to get the output. So, in reinforcement learning, there is no predefined data set given to the machine. The agent does all the work from scratch.

Training

In Reinforcement learning, there is no predefined data, and the whole reinforcement learning process itself is a training and testing phase. Since there is no predefined data given to the machine, It has to learn everything on its own and it starts by exploring and collecting data.

Aim

In reinforcement learning the agent here is a lot like a human child, just like how a baby is clueless about the world initially. The agent also has no idea about its environment. But as it explores the environment it starts learning. It learns from the mistakes it makes and it basically learns from its experience.

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Approach

The approach followed by reinforcement learning is a trial and error method. The trial and error method best explain reinforcement learning because the agent has to try out all possible actions to learn about its environment and to get maximum rewards.

Output Feedback

In reinforcement learning the feedback is in the form of rewards or punishments from the environment. So, when an agent takes a suitable action, it will get a corresponding reward for that action. But if the action is wrong then it gets a punishment. So, rewards and punishments can be thought with respect to a game. Now in a game when you win a state you get extra coins, but when you fail you have to go back to the same state and try again.

Popular Algorithms

Few algorithms include Q learning and the state action reward state action (SARSA) Algorithm.

Applications

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Reinforcement learning is used in self-driving cars, in building games and one famous example is the AlphaGo game.

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