All about Reinforcement Learning
All about Reinforcement Learning
Reinforcement learning is one of the machine learning methods which works on the principle based on rewards and penalties. A reinforcement learning agent will be able to understand and interpret its environment, and take actions, and can learn by trial and error. In reinforcement learning, developers develop a method that is based on rewarding behaviors and punishing the negative behaviors.
Reinforcement learning provides positive values when there is a desired positive action to encourage the agent and provides negative values as penalties for an undesired action. To achieve an optimal solution, reinforcement learning programs for seeking long-term and maximum overall reward.
Such long-term goals help the agents from stalling on lesser goals. With the learning time, the agent will learn to avoid the negative and seek the positive action. This is adopted in Artificial Intelligence to direct unsupervised machine learning through rewards and penalties.
In the field of Robotics, reinforcement learning has a very important role. This type of learning technique provides robotic machines with the ability to learn tasks that the human trainer cannot demonstrate and to adapt a skill to do a new task or maybe to achieve optimization even though analytical formulation is unavailable.
When considering different machine learning, they are classified into supervised learning, unsupervised learning, semi-supervised and to reinforcement learning. Reinforcement learning takes a different approach altogether. It places an agent in an environment with parameters defining activity that is deemed positive and negative in nature. This way, it is similar to supervised learning where the algorithm provides clear goals and provides a definition of rewards and penalties.
This also means that level of programming required is more than unsupervised learning. Once the parameters is defined and set, the algorithm operates pretty much on its own, making it superior to supervised learning. Sometimes, reinforcement learning is referred to as a branch of semi-supervised but, in reality, it is acknowledged as its own type of machine learning.
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