Reinforcement learning is an area of Machine Learning. In reinforcement learning, an artificial intelligence faces a game-like situation.
And then the computer employs trial and error to come up with a solution to the problem. To get a machine to do what a programmer wants, artificial intelligence gets either a reward or a penalty for the actions it performs.
Firstly, its goal is to maximize the total reward.
Challenges with reinforcement learning
The main challenge in RL lays in preparing the simulation environment, which is highly dependent on the task to be performed.
It has challenges such as
- The sample efficiency problem
- Stability of training
- The exploration problems
- Meta-learning and representation learning for the generality of reinforcement learning methods across tasks
Types of Reinforcement:
- Positive
It is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior.
In other words, it has a positive effect on behavior.
2. Negative
Negative Reinforcement is defined as strengthening of a behavior because a negative condition is stopped or avoided.
Applications of Reinforcement Learning
- As I mentioned earlier, RL is the best technology. we can use it for game playing. It can even beat world champions.
- It can be used effectively to determine the best move to make in a game.
- Used in robotics for industrial automation.
- Used in machine learning and data processing.
- We use it to create training systems that provide custom instruction and materials according to the requirement of students.
- We use this technology to learn robots.
Advantages of reinforcement learning
- Maximizes Performance
- Sustain Change for a long period of time
- Increases Behavior
- Provide defiance to minimum standard of performance
- This learning model is very similar to the learning of human beings. Hence, it is close to achieving perfection.
- The model can correct the errors that occurred during the training process.
- It can create the perfect model to solve a particular problem.
- It can be useful when the only way to collect information about the environment is to interact with it.
Disadvantages of reinforcement learning
- Too much Reinforcement can lead to overload of states which can diminish the results
- It Only provides enough to meet up the minimum behavior.
- Reinforcement learning as a framework is wrong in many different ways, but it is precisely this quality that makes it useful.
- It is not preferable to use for solving simple problems.
- It needs a lot of data and a lot of computation. That is why it works really well in video games because one can play the game again and again.
Summary
In conclusion, reinforcement learning is a type of machine learning in which the machine learns by itself after making many mistakes and correcting them.
In short, we have learnt about reinforcement learning. We explained its challenges, advantages, disadvantages and more.
