Reinforcement Learning

"What reinforcement we may gain from hope; if not, what resolution from despair. "

Reinforcement learning is kind of machine learning. So before going into the depth of Reinforcement learning , first see what is machine learning .

Machine learning: Definition

Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data .

Machine Learning Algorithms :

With respect to the feedback type to learner, the machine learning can be divided into three groups :

• Supervised learning — In which the algorithm generates a function that maps inputs to desired outputs. We have both input and output. We can say that Supervised learning is task driven . It basically deals with classification.

• Unsupervised learning — An agent which models a set of inputs: labeled examples are not available. Here we try to make cluster of similar type of data and then fed them to the Neural Network. The neural network proceed until it reaches to a certain minim error or a predefined epochs. Model is prepared once in the starting of learning. It is data driven . It basically deals with clustering.

• Reinforcement learning — This approach is very close to human learning. In Reinforcement learning the algorithm learns a policy of how to act in a given environment. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm.

Difference in Supervised , unsupervised and Reinforcement learning???

Supervised learning is task driven, that in advance we know that for a particular input what is the output, It basically deals with classification.

On the other hand in unsupervised learning is data driven i.e input is not mapped to the output . It basically deals with clustering.

While Reinforcement learning emphasizes learning feedback that evaluates the learner's performance without providing standards of correctness in the form of behavioral targets.

The following example will explain u more :

Supervised Learning

Step: 1
Teacher: Does picture 1 show a tree or a bird ?
Learner: A flower.
Teacher: No, it’s a tree .

Step: 2
Teacher: Does picture 2 show a car or a bike?
Learner: A car.
Teacher: Yes, it’s a car.

Step: 3 ....

Reinforcement Learning

Step: 1
World: You are in state 4. Choose action A or C.
Learner: Action A.
World: Your reward is 100.

Step: 2

World: You are in state 27. Choose action B or E.
Learner: Action B.
World: Your reward is 50.

Step: 3 ....

so we can say that reinforcement learning doesn’t tell that the action is best of not. It only tell if it is good or bad by assigning scalar rewards .

Meaning of Reinforcement:

The occurrence of an event, in the proper relation to a response, that tends to increase the probability that the response will occur again in the same situation.

So we can define Reinforcement learning as a problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. Reinforcement learning emphasizes learning feedback that evaluates the learner's performance without providing standards of correctness in the form of behavioral targets.

The best example of reinforcement learning is Bicycle learning