What is Reinforcement Learning?

Introduction

Reinforcement Learning (RL) is a form of machine learning in which an agent appears to learn through actions executed in an environment. RL is based on decision making and learning as opposed to training on labeled datasets as in the case of supervised learning. The objective is to increase the total reward attained over a certain period.

In RL, an agent learns through the associations of its actions by obtaining rewards or punishments. The agent's resources will be spent depending on the optimal paths it has learned. This method is applied in robotics and gaming as well as internal systems of self-drive vehicles where the real-time decisions are implemented.

Conclusion

In conclusion, RL gives the true gist that machines can learn from their experience which is a great application in addressing complex problems that are ever changing. With the increased development of RL, its contribution towards enhancement of Ai technologies will be very vital in the development of intelligent systems enhanced with a lot of automation.

Reference:

Reinforcement learning - GeeksforGeeks