Does a form of reinforcement learning exist where an agent can only receive reward based on its current state, rather than a perceived future reward assessed by reasoning over the agent's possible future actions? The reward must still result in the evolution of near optimal policy chains.
The literature knows two different techniques: model-based reinforcement learning and model-free reinforcement learning. To make things more complicated, the model-free approach can be enhanced with a reward model. A reward model is equal to a reward function which can be learned with learning from demonstration. Sometimes, it's called inverse reinforcement learning, because at the beginning the reward model isn't available.
To answer the initial question directly, the answer is yes; reinforcement learning without a model is possible. The reward model has to be created on the fly with incoming data. In theory, this technique is able to solve more complex problems, which are going beyond a line following robot.
 What's the difference between model-free and model-based reinforcement learning?, What's the difference between model-free and model-based reinforcement learning?
 Section Injecting Human Knowledge, https://ai.stackexchange.com/a/11821/11571
 Expressing Arbitrary Reward Functions as Potential-Based Advice (PBA), Expressing Arbitrary Reward Functions as Potential-Based Advice (PBA)