# How to approach Model and policy in reinforcement learning?

Hey I am currently taking Stanford cs243 reinforcement learning course in Youtube to learn reinforcement learning in that I understand that policy is something like a function which get a specific state as input and return a action but I am confused that policy will return a action from the state space of the action which likelihood is high now my question is that for deterministic process how the policy chosen and stochastic policy is it return a single action or max(policy(s)=probability distribution of the actions in that specific state) and is that a joint probability or conditional probability?

How to approach a model in RL?

I understood that in a transition model it receives a observation or some information about the world and the state,action if possible now it will give some feedback to agent or a idea how the next state will be or future world idea?

So how to differentiate between a decision model and a reward model?

so help me to validate these to clarify my understanding about the model and policy.