Determine the action space. These actions can be discrete, like pressing a button or continuous as in the value within an interval. Actions can be as many as you’d like to have performed simultaneously, and keep in mind that some actions can be invalid under specific conditions.
Determine the reward. The reward must correspond to the goal we want to accomplish—for example, a positive reward when winning and a negative reward when losing. You can also give a positive reward for each action that leads to a target or a negative reward when moving far away from the target.
Define the Deep Reinforcement Learning algorithm. The speed of the agent’s learning capabilities depends on the algorithm as well as whether the agent can learn at all. For each task, it’s necessary to study the features and how applicable they are in unique contexts.