Reinforcement learning

Remember how you learned to ride a bicycle in your childhood? It was a trial and error process, right? You tried to balance yourself, and each time you did something wrong, you tipped off the bicycle. But, you learned from your mistakes, and eventually, you were able to ride without falling. In the same way, Reinforcement learning does the same! An agent is exposed to an environment where it takes action from a list of possible actions, which leads to a change in the state of the agent. A state is the current situation of the environment the agent is in. For every action, the agent receives an award. Whenever the received reward is positive, it signifies the agent has taken the correct step, and when the reward is negative, it signifies a mistake. The agent follows a policy, a reinforcement learning algorithm through which the agent determines next actions considering the current state. Reinforcement learning is the true form of artificial intelligence, inspired by a human's way of learning through trial and error. Think of yourself as the agent and the bicycle the environment! Discussing reinforcement learning algorithms here is beyond the scope of this book, so let's shift focus back to deep learning!