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Randomized and sequential immediate rewards

I am reading about reinforcement learning (RL) and I am struggling to understand some concepts:
How to use immediate rewards in sequential decision making?
How to generate the actions of an RL algorithm?
How to break the sequence of immediate rewards and generate a random number sequence as the feedback?

A:

This is a combination of two questions and I am assuming the environments that you are mentioning is the Machine Learning environments.

How to use immediate rewards in sequential decision making?

There is nothing wrong in using immediate rewards. They are simply the states that the system enters.
Note: With the immediate rewards, you are not learning a policy. The action that you are taking is the immediate action and the reward is simply the next state that will enter into the environment and states are the immediate rewards.

How to generate the actions of an RL algorithm?

They can be generated using the policy network (Outputs) or the action space map (Action map).
You can generate the actions of an RL agent by initializing the environment with a policy network (or a Q-function network) and the agent has to learn how to’replay’ or in the case of models, follow the policy. The actions are generated by the output of the policy network (or Q network) and the goal is to maximize the
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