I will certainly agree that a big problem for the FEP is related to its presentation. They start with the equations of mathematical physics and show how to get from there to information theory, inference, beliefs, etc. This is because they are trying to get from matter to mind. But they could have gone the other way since all the equations of mathematical physics have an information theoretic derivation that includes a notion of free energy. This means that all the stuff about Langevin dynamics of sparsely connected systems (the 'particular' fep) could have been included as a footnote in a much simpler derivation.
As you note, the other problem with the FEP is that it seems to add very little to the dominant RL framework. I would argue that this is because they are really not interested in designing better agents, but rather in figuring out what it means for mind to arise from matter. So basically it is physics inspired philosophy of mind, which does sound like something that has no utility whatsoever. But explanatory paradigms can open up new ways of thinking.
For example, relevant to your interests, it turns out that the FEP definition of an agent has the potential to bypass one of the more troubling AI safety concerns associated with RL. When using RL there is a substantial concern that straight-up optimizing a reward function can lead to undesirable results, i.e. the imperative to 'end world hunger' leads to 'kill all humans'. In contrast, in the standard formulation of the FEP the reward function is replaced by a stationary distribution over actions and outcomes. This suggests the following paradigm for developing a safer AI agent. Observe human decision making in some area to get a stationary distribution over actions and outcomes that are considered acceptable but perhaps not optimal. Optimize the free energy of the expected future (FEEF) applied to the observed distribution of actions and outcomes (instead of just outcomes as is usually done) to train an agent to reproduce human decision-making behavior. Assuming it works you now have an automated decision maker that, on average, replicates human behavior, i.e you have an agent that is weakly equivalent to the average human. Now suppose that there are certain outcomes that we would like to make happen more frequently than human decision-makers have been able to achieve, but don't want the algorithm to take any drastic actions. No problem: train a second agent to produce this new distribution of outcomes while keeping the stationary distribution over actions the same.
This is not guaranteed to work as some outcome distributions are inaccessible, but one could conceive an iterative process where you explore the space of accessible outcome distributions by slightly perturbing the outcome distribution and retraining and repeating...
There is a great deal of confusion regarding the whole point of the FEP research program. Is it a tautology, does it apply to flames, etc. This is unfortunate because the goal of the research program is actually quite interesting: to come up with a good definition of an agent (or any other thing for that matter). That is why FEP proponents embrace the tautology criticism: they are proposing a mathematical definition of 'things' (using markov blankets and langevin dynamics) in order to construct precise mathematical notions of more complex and squishy concepts that separate life from non-life. Moreover, they seek to do so in a manner that is compatible with known physical theory. It may seem like overkill to try to nail down how a physical system can form a belief, but its actually pretty critical for anyone who is not a dualist. Moreover, because we dont currently have such a mathematical framework we have no idea if the manner in which we discuss life and thinking is even coherent. This about Russel's paradox. Prior to late 19th century it was considered so intuitively obvious that a set could be defined by a property that the so called axiom of unrestricted comprehension literally went without saying. Only in the attempt to formalize set theory was it discovered that this axiom had to go. By analogy, only in the attempt to construct formal description of how matter can form beliefs do we have any chance of determining if our notion of 'belief' is actually consistent with physical theories.
While I have no idea how to accomplish such an ambitious goal, it seems clear to me that the reinforcement learning paradigm is not suited to the task. This is because, in such a setting, definitions matter and the RL definition of an agent leaves a lot to be desired. In RL, an agent is defined by (a) its sensory and action space, (b) its inference engine, and (c) the reward function it is trying to maximize. Ignoring the matter of identifying sensory and action space, it should be clear this is a practical definition not a principled one as it is under-constrained. This isn't just because I can add a constant to reward without altering policy or something silly like that, it is because (1) it is not obvious how to identify the sensory and action space and (2) inference and reward are fundamentally conflated. Item (1) leads to questions like does my brain end at the base of my skull, the tips of my fingers, or the items I have arranged on my desk. The Markov blanket component of the FEP attempts to address this, and while I think it still needs work it has the right flavor. Item (2), however, is much more problematic. In RL policies are computed by convolving beliefs (the output of the inference engine) with reward and selecting the best option. This convolution + max operation means that if your model fails to predict behavior it could be because you were wrong about the inference engine or wrong about the reward function. Unfortunately, it is impossible to determine which you were wrong about without additional assumptions. For example, in MaxEntInverseRL one has to assume (incredibly) that inference is Bayes optimal and (reasonably) that equally rewarding paths are equally likely to occur. Regardless, this kind of ambiguity is a hallmark of a bad definition because it relies on a function from beliefs and reward to observations of behavior that is not uniquely invertible.
In contrast, FEP advocates propose a somewhat 'better' definition of an agent. This is accomplished by identifying the necessary properties of sensor and action spaces, i.e. they form a Markov blanket and, in a manner similar to that used in systems identification theory, define an agent's type by the statistics of that blanket. They then replace arbitrary reward functions with negative surprise. Though it doesn't quite work for very technical reasons, this has the flavor of a good definition and a necessary principle. After all, if an object or agent type is defined by the statistics of its boundary, then clearly a necessary description of what an agent is doing is that it is not straying too far from its definition.
That was more than I intended to write, but the point is that precision and consistency checks require good definitions, i.e. a tautology. On that front, the FEP is currently the only game in town. It's not a perfect principle and its presentation leaves much to be desired, but it seems to me that something very much like it will be needed if we ever wish to understand the relationship between 'mind' and matter.
The short answer is that, in a POMDP setting, FEP agents and RL agents can be mapped one onto the other via appropriate choice of reward function and inference algorithm. One of the goals of the FEP is to come with a normative definition of the reward function (google the misleadingly titled "optimal control without cost functions" paper or, for a non-FEP version of the same, thing google the accurately titled: "Revisiting Maximum Entropy Inverse Reinforcement Learning"). Despite the very different approaches, the underlying mathematics is very similar as both are strongly tied to KL control theory and Jaynes' maximum entropy principle. But the ultimate difference between FEP and RL in a POMDP setting is how an agent is defined. RL needs an inference algorithm and a reward function that operates on action and outcomes, R(o,a). The FEP needs stationary blanket statistics, p(o,a), and nothing else. The inverse reinforcement paper shows how to go from p(o,a) to a unique R(o,a) assuming a bayes optimal RL agent in a MDP setting. Similarly, if you start with R(o,a) and optimize it, you get a stationary distribution, p(o,a). This distribution is also unique under some 'mild' conditions. So they are more or less equivalent in terms of expressive power. Indeed, you can generalize all this crap to show any subsystem of any physical system can be mathematically described as Bayes optimal RL agent. You can even identify the reward function with a little work. I believe this is why we intuitively anthropomorphize physical systems, i.e. when we say things like they system is "seeking" a minimum energy state.
But regardless, from a pragmatic perspective they are equally expressive mathematical systems. The advantage of one over the other depends upon your prior knowledge and goals. If you know the reward function and have knowledge of how the world works use RL. If you know the reward function but are in a POMDP setting without knowledge of how the world works, use an information seeking version of RL (maxentRL or BayesianRL). If you dont know the reward function but do know how the world works and have observations of behavior use max ent inverseRL).
The problem with RL is that its unclear how to use it when you don't know how the world works and you don't know what the reward function is, but do have observations of behavior. This is the situation when you are modeling behavior as in the url you cited. In this setting, we don't know what model humans are using to form their inferences and we don't know what motivates their behavior. If we are lucky we can glean some notion of their policy by observing behavior, but usually that notion is very coarse i.e. we may only know the average distribution of their actions and observations, p(o,a). The utility of the FEP is that p(o,a) defines the agent all by itself. This means we can start with a policy and infer both belief and reward. This is not something RL was designed to do. RL is for going from reward and belief (or belief formation rules) to policy, not the other way around. IRL can go backward, but only if your beliefs are Bayes optimal.
As for the human brain, I am fully committed to the Helmholtzian notion that the brain is a statistical learning machine as in the Bayesian brain hypothesis with the added caveat that it is important to remember that the brain is massively suboptimal.