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Reinforcement learning is a type of machine learning that has come into the limelight recently. This is also a branch of artificial intelligence. This type of learning allows software agents and machines to determine the behavior of the data within a specific context, to enhance performance and efficiency. The machine would need to be given some feedback that would help the machine or the agent to learn its behavior better. This is called the reinforcement signal.
There are different algorithms that look at this issue. Reinforcement learning is always defined by a specific problem, and the solutions are all categorized into Reinforcement Algorithms. The agent or the machine is always supposed to decide what the best action is based on the current state of the data. When this step is repeated, this problem is called the Markov Decision Process. So, why use reinforcement learning.
This type of learning allows a software agent or machine to learn from the behavior that is most suitable to the engineer or user based on the feedback provided by the environment. The machine learns this behavior at the same time, or it can adapt to the situation as it changes. If the engineer models the problem with care, the reinforcement algorithms can be used to achieve the global optimum. This is the type of behavior that can be used to maximize the reward.
This method of learning shows that there is very little need for human experts who know the workings of the domain. Very little time is spent on designing solutions to problems fed to the system since there would be no need to craft complex sets of rules. All the process would need is someone who is familiar with the concept of reinforcement learning.
As mentioned earlier, there are some solutions to different problems that are fed to the machine. The most common problem is the one that allows a software agent to select an action that will maximize the reward in the future. These types of algorithms are called infinite horizon problems.
The machine performs this action by learning to estimate the value of the particular state of action. The estimate is then adjusted by identifying the next reward. If every state and action is tried some times, this will allow the optimal solution or policy to be defined – which is the only way to maximize the next state or action that is picked out.
There are some challenges faced by engineers who are researching reinforcement learning. It is often very expensive to store the output values obtained from each action or state. When these problems are looked at in detail, techniques like neural networks and decision trees are used. There are numerous situations when the engineer can introduce the estimations to ensure that the output finally obtained would minimize the impact of wrong solutions on the quality of the solution.
Problems are modular, and similar patterns appear in the problems, and the modularity can also be introduced to enhance or avoid learning everything again. Approaching the problem using hierarchical approaches is proving to be difficult. Due to limited perception, it has become difficult to determine the state of the problem. This also tends to affect the performance and efficiency of the algorithm, and a lot of work has been performed to compensate for this perceptual aliasing.
Reinforcement learning can be applied in some fields and actions since the problem specification could be generic. A large number of problems are often mapped to a decision process, especially problems that arise due to artificial intelligence. The same theory can be applied to different problems that are specific to some areas with little or no effort.
This application ranges right from robot navigation where the behavior to avoid collision can be avoided by identifying the efficient motor combination to control robotic arms. This can be learned by looking at the negative feedback that comes from bumping into obstacles. Logic from game theory can be used in reinforcement learning since these games are often defined as a sequence and combination of decisions.