Before understanding how to use Reinforcement Learning, we need to understand what it means. It is an aspect of Machine Learning which is used to take suitable actions to maximize rewards. It has been employed by several organizations to find the best possible solution to a problem hence improving business processes.
There are different applications of Reinforcement Learning in various business departments. The main aim is to enhance the efficiency of the processes lies there. Let us understand better using two examples;
1) An application of the same is in a training program of Robots. It can help a robot to learn policies that contribute to taking necessary actions by using raw video images.
So the robots generate motor torques based on the input images. Like, suppose a robot wants to learn how to walk. It first starts by taking longer steps, but it falls. So it triggers negative feedback.
The next action of the robot is to change its style of walking and try once again. Hence, in this way, finally, it can learn how to walk correctly without any unbalance or falls. It is possible by learning from its past actions and the number of positive feedback or rewards.
2) Another example would be in training the program of the object in a game world that learns by itself about how to move ahead. There is no intervention of any supervisor!
So let us suppose a 2d game having an object or the agent is present in an environment made up of cells of similar sizes surrounding everywhere. Now the task of the agent is to move in between different cells. Also, avoiding the obstacles, thereby to reach the final goal or target cell. So, the policy here is it can move only one step or cell in a chance!
Hence, to move further every time, the object has four directions up, down, left, and right to choose anyone to proceed ahead! Now, after moving to a new cell, the object might receive a reward if it has been transferred correctly to reach the desired goal. Otherwise, it will come back and try once again.
Further, the object collects rewards continuously to maximize its compensation. Hence, at the same time, it can learn by assigning some preference on the previous moves performed by the same. Further, the object can optimize its action of a state that has been moved, as part of its learning process.
So, everything is managed only based on the rewards received. It is to identify what action would give the highest reward by considering the set of states. Something important that makes the training the algorithm of program optimal to get the job done!
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