Reinforcement Learning with TensorFlow

Reinforcement learning

Reinforcement learning is a branch of artificial intelligence that deals with an agent that perceives the information of the environment in the form of state spaces and action spaces, and acts on the environment thereby resulting in a new state and receiving a reward as feedback for that action. This received reward is assigned to the new state. Just like when we had to minimize the cost function in order to train our neural network, here the reinforcement learning agent has to maximize the overall reward to find the the optimal policy to solve a particular task.

How this is different from supervised and unsupervised learning?

In supervised learning, the training dataset has input features, X, and their corresponding ...