In my model there is no relationship between the action and the following state,and that makes me wonder if its ok to think about RL in this situation at all.
This seems strange. What do actions do if not change state? Note that agents don't have to necessarily know how their actions will change their state. Similarly, actions could change the state imperfectly (a robots treads could skid out so it doesn't actually move when it tries to). In fact, some algorithms are specifically designed for this uncertainty.
In any case, even if the agents are moving around the state space without having any control, it can still learn the rewards for the different states. Indeed, many RL algorithms involve moving around the state space semi-randomly to figure out what the rewards are.
I do not have a clear definition for States as they might happen a few times or not happen at all because Agents move around and they might never face a situation
You might consider expanding what goes into what you consider to be a "state". For instance, the position seems like it should definitely go into the variables identifying a state. Not all states need to have rewards (although good RL algorithms typically infer a measure of goodness of neutral states).
I would recommend clearly defining the variables that determine an agent's state. For instance, the state space could be current-patch X internal-variable-value X other-agents-present
. In the simplest case, the agent can observe all of the variables that make up their state. However, there are algorithms that don't require this. An agent should always be in a state, even if the state has no reward value.
Now, concerning unknown reward. That's actually totally okay. Reward can be a random variable. In that case, a simple way to apply standard RL algorithms would be to use the expected value of the variable when making decisions. If the distribution is unknown, then the algorithm could just use the mean of the rewards observed so far.
Alternatively, you could include the variables that determine the reward in the definition of the state. That way, if the reward changes, then it is literally in a different state. For example, suppose a robot is on top of a building. It needs to get to the top of the building in front of it. If it just moves forward, it falls to ground. Thus, that state has a very low reward. However, if it first places a plank that goes from one building to the other, and then moves forward, the reward changes. To represent this, we could include plank-in-place
as a variable so that putting the board in place actually changes the robot's current state and the state that would result from moving forward. Thus, the reward itself has not changed; it's just in a different state.
Hopefully this helps!
UPDATE 2/7/2018: A recent upvote reminded me of the existence of this question. In the years since it was asked, I've actually dived into RL in NetLogo to a much greater extent. In particular, I've made a python extension for NetLogo, primarily to make it easier to integrate machine learning algorithms in with model. One of the demos of the extension trains a collection of agents using deep Q-learning as the model runs.