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Reinforcement Learning involves the mechanism of reward and punishment for the process of learning. In this type of learning, the objective is to maximize the reward and minimize the punishment. In Reinforcement Learning Errors help you learn because they have a penalty added (cost, loss of time, regret, pain, and so on).

Ex. when computers learn to play video games by themselves.

Figure 1.10 Reinforcement learning.

Reinforcement learning is connected to the applications for which the algorithm must make decisions and where the decisions held consequences. In the human world, it is similar to learning by trial and error. In cognitive computing, reinforcement learning is mostly used where numerous variables in the model are difficult to represent and the model has to do a sequence of tasks. For example Self-driving cars.

In reinforcement learning, we have an agent that acts in the environment as shown in Figure 1.10. The agent can take action and this action can impact the environment. In a particular stage, the agent takes an action and the environment goes to a new state and gives some reward to the agent, that reward may be positive can be a negative reward or penalty or can be nothing at that particular time step. But the agent is continually acting in this world.

The model finds a relation between the reward and the sequence of tasks, which lead to getting a reward.