Deep Learning with Keras

Maximizing future rewards

As an agent, our objective is to maximize the total reward from each game. The total reward can be represented as follows:

In order to maximize the total reward, the agent should try to maximize the total reward from any time point t in the game. The total reward at time step t is given by R_t and is represented as:

However, it is harder to predict the value of the rewards the further we go into the future. In order to take this into consideration, our agent should try to maximize the total discounted future reward ...