Hands-On Artificial Intelligence for IoT

Deep reinforcement learning

RL algorithms can be classified into two, based on what they iterate/approximate:

• Value-based methods: In these methods, the algorithms take the action that maximizes the value function. The agent here learns to predict how good a given state or action would be. Hence, here, the aim is to find the optimal value. An example of the value-based method is Q-learning. Consider, for example, our RL agent in a maze: assuming that the value of each state is the negative of the number of steps needed to reach from that box to the goal, then, at each time step, the agent will choose the action that takes it to a state with optimal value, as in the following diagram. So, starting from a value of -6, it'll move to -5, -4 ...