Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or a penalty.
- In Reinforcement Learning, the agent learns automatically using feedback without any labeled data, unlike supervised learning.
- Since there is no labeled data, so the agent is bound to learn by its experience only.
- RL solves a specific type of problem where decision-making is sequential, and the goal is long-term, such as game-playing, robotics, etc.
- The agent interacts with the environment and explores it by itself. The primary goal of an agent in reinforcement learning is to improve performance by getting the maximum positive rewards.
Main points in Reinforcement learning –
- Input: The input should be an initial state from which the model will start
- Output: There are many possible outputs as there are a variety of solutions to a particular problem
- Training: The training is based upon the input, The model will return a state and the user will decide to reward or punish the model based on its output.
- The model keeps continues to learn.
- The best solution is decided based on the maximum reward.
Types of Reinforcement: There are two types of Reinforcement:
- Positive Reinforcement is defined as when an event, occurs due to a particular behavior, and increases the strength and the frequency of the behavior. In other words, it has a positive effect on behavior.
- Advantages of reinforcement learning are:
- Maximizes Performance
- Sustain Change for a long period of time
- Too much Reinforcement can lead to an overload of states which can diminish the results
- Negative Reinforcement is defined as the strengthening of behavior because a negative condition is stopped or avoided.
- Advantages of reinforcement learning:
- Increases Behavior
- Provide defiance to a minimum standard of performance
- It Only provides enough to meet up the minimum behavior