Reinforcement Learning and Supervised Learning

The Reinforcement Learning and Supervised Learning both are the part of machine learning, but both types of learnings are far opposite to each other. The RL agents interact with the environment, explore it, take action, and get rewarded. Whereas supervised learning algorithms learn from the labeled dataset and, on the basis of the training, predict the output.

The difference table between RL and Supervised learning

Reinforcement Learning

RL works by interacting with the environment.

The RL algorithm works like the human brain works when making some decisions.

There is no labeled dataset is present.

No previous training is provided to the learning agent.

RL helps to take decisions sequentially.

Supervised Learning

Supervised learning works on the existing dataset.

Supervised Learning works as when a human learns things in the supervision of a guide.

The labeled dataset is present.

Training is provided to the algorithm so that it can predict the output.

In Supervised learning, decisions are made when input is given.

The difference table between RL and Supervised learning

1. Robotics: RL is used in Robot navigation, Robo-soccer, walking, juggling, etc.
2. Control: RL can be used for adaptive control such as Factory processes, admission control in telecommunication, and Helicopter pilot is an example of reinforcement learning.
3. Game Playing: RL can be used in Game playing such as tic-tac-toe, chess, etc.
4. Chemistry: RL can be used for optimizing the chemical reactions.
5. Business: RL is now used for business strategy planning.
6. Manufacturing: In various automobile manufacturing companies, the robots use deep reinforcement learning to pick goods and put them in some containers.
7. Finance Sector: The RL is currently used in the finance sector for evaluating trading strategies.