Understand reinforcement learning and its application scenarios

The best way to train a dog is to use a reward system to reward it for good behavior and punish it for doing wrong things. The same strategy can be used for machine learning, called reinforcement learning.

Reinforcement learning is one of the branches of machine learning, which trains models through decision-making to find the best solution to the problem.

In order to improve the accuracy of the model, positive rewards can be used to encourage the algorithm to approach the correct answer, while negative rewards can be given to punish deviations from the target.

You only need to clarify the goals and then model the data. The model starts to interact with the data and proposes solutions on its own without manual intervention.

Reinforcement Learning Example

We still take dog training as an example. We provide rewards such as dog biscuits to let the dog perform various actions.

The dog will follow a certain strategy to pursue rewards, so it will obey commands and learn new actions, such as begging.

Dogs love to run around, play and explore their surroundings. In reinforcement learning algorithms, this behavior is called exploration. Dogs will tend to maximize their own rewards, which is called exploiting. However, there is a trade-off between exploration and exploitation, as exploration may bring less rewards.

Important Terms in Reinforcement Learning

• Agent: An agent is a model trained through reinforcement learning
• Environment: The training situation to which the model must be optimized is called its Environment
• Actions: All possible steps the model can take
• Status: The current position/status returned by the model
• Rewards: To help the model move in the right direction, it Rewards/points are given to evaluate certain actions
• Strategy: The strategy determines how the agent behaves at any time. It acts as a mapping between actions and current state

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