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Reinforcement learning is the most critical area in machine learning. There are many machine learning models that are trained to make a series of decisions. The agent will try to attain the goal even in a complicated environment. The computer would make use of the trial and error method to find the solutions to the problem. If the programmer wants the machine to do what he/she wants, the artificial intelligence would either get rewards or penalties. However, the aim is to increase the rewards. Reinforcement learning is all about taking the right action to increase the rewards. This is used by the software and machine to decide the best possible behaviour for a specific situation. Reinforcement learning is different from that supervised learning. In supervised learning, the training data would have the answer so that the model would be trained to give the correct answer. When it comes to reinforcement learning, there is no answer that is available. However, the reinforcement agent would decide what actions must be taken to carry out the task. When there is no training dataset available, then the machine would learn from the experience.
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When taking the example of an agent and a reward the agent has to find the best possible path to detect the diamond by crossing the hurdles. Imagine that there are three different images such as a robot, fire, and a reward (diamond). The main goal of the robot is to find the diamond without getting caught in the fire. The robot will learn various possible paths to reach the diamond and choose the best path that would help to get the rewards, but with fewer hurdles. Every step that a robot takes would give a reward and every wrong step that is taken by the robot would deduct the reward. The rewards that are earned by the robot would be calculated once the goal is reached.
No supervisor exists. There would be a real number or a reward
Decision-making would be in a sequence
Time plays a critical role in solving the reinforcement problems
Delayed in feedback, there is no instant feedback that you receive.
The action taken by the agent would decide the data you receive next.
The reinforcement learning would work based on the reward and punishment basis. The agent would get the reward when he/she takes the right move and at the same time, the agent would get the punishment, when he/she would take the wrong move. This will help the agent to reduce the number of wrong moves and increase the number of right ones. A few of the real-world applications of reinforcement learning include:
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Adaptability - Reinforcement learning algorithms can adapt to changing environments and adjust their behaviour accordingly.
Real-time Decision Making - Reinforcement learning algorithms can make decisions in real-time and respond to changing conditions.
Problem-Solving - Reinforcement learning algorithms can solve complex problems by exploring different strategies and learning from their outcomes.
Improved Performance - Reinforcement learning algorithms can continuously improve their performance over time through trial and error.
There are two different types of reinforcement learning available. These include:
Positive reinforcement learning - This is the event that would happen due to a particular behaviour. It will boost the strength and frequency of the behaviour and would have a positive impact on the action that is taken by the agent. It is the best type of reinforcement that helps you boost performance and embrace the changes for a long time. If you reinforce a lot, it would lead to an over-optimization state that would have an impact on the results.
Negative reinforcement learning - The negative reinforcement would strengthen the behaviour that would occur due to the negative condition, which you must prevent. This will let you define the minimum level of performance. The main drawback of this type is that it would offer you enough to meet the minimum behaviour.
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