Reinforcement learning (RL) combines a control problem with statistical estimation: the system dynamics are not known to the agent, but can be learned through experience. A recent line of research casts 'RL as inference' and suggests a particular framework to generalize the RL problem as probabilistic inference. Our paper surfaces a key shortcoming in that approach, and clarifies the sense in which RL can be coherently cast as an inference problem. In particular, an RL agent must consider the effects of its actions upon future rewards and observations: the exploration-exploitation tradeoff. In all but the most simple settings, the resulting inference is computationally intractable so that practical RL algorithms must resort to approximation. We demonstrate that the popular 'RL as inference' approximation can perform poorly in even very basic problems. However, we show that with a small modification the framework does yield algorithms that can provably perform well, and we show that the resulting algorithm is equivalent to the recently proposed K-learning, which we further connect with Thompson sampling.
The paper at hand presents an alternative view on reinforcement learning as probabilistic inference (or equivalently maximum entropy reinforcement learning). With respect to other formulations of this view (e.g. Levine, 2018; I am referring to the references of the paper here), the paper identifies a shortcoming in the disregard of the agent’s epistemic uncertainty (which seems to refer to the uncertainty with respect to the underlying MDP). It is argued, that algorithms based on the prevailing probabilistic formulation (e.g. soft Q-learning) suffer from suboptimal exploration.
The paper thus compares maximum entropy RL to K-learning (O’Donoghue, 2018), which is taken to address the issue of suboptimal exploration due to its temperature scheduling and its inclusion of state-action pair counts in the reward signal. 
As its technical contribution, the paper re-interprets K-learning via the latent variable denoting optimality employed in Levine (2018) and introduces a theorem bounding the distance between the policies of Thompson sampling and K-learning. Empirical validation of the claims is provided via experiments on an engineered bandit problem and the tabular MDP (i.e. DeepSea from Osband et al., 2017), as well as via soft Q-learning results on the recently suggested bsuite (Osband et al., 2019).
I consider this paper a weak reject. This is in light of me finding it very hard to follow the papers main claims and arguments, even though it positions itself as communicating connections (“making sense”) in prior work, rather than presenting a novel algorithm. While this is in part due to the complicated issue and math being discussed (and the paper probably catering to a very narrow audience), the paper in its current state does seem to hinder understanding as well.
On the positive side, I do appreciate the intention of the paper, namely to connect RL as probabilistic inference, Thompson sampling and K-learning. In my opinion, this can be taken as a valuable addition to the current understanding of these approaches. Also, I like the experiments as they are specifically constructed to support the claims of the paper.
On the negative side, vague language, missing assumptions and lax notation seem to hinder the understanding of the paper to a considerable extend: e.g. it is stated, that “we connect the resulting algorithm with […] K-learning”. However, I do not recognize a new algorithm being provided. Instead the paper argues in favor of K-learning. The assumptions that come with K-learning are not mentioned. The restriction of K-learning to tabular RL is taken to be understood implicitly (whereas RL as probabilistic inference seems applicable with function approximation also, which is not mentioned in the comparison). The paper always talks of shortcomings (plural) of RL as probabilistic inference, but only provides one argument (suboptimal exploration) with respect to this. RL as probabilistic inference is introduced in a different form as in prior literature (i.e. Equation 6), while the derivation in the Appendix spanning the differences in notation being hard to follow due to (maybe minor?) notational issues (e.g. x and y seem to have replaced s’ and a; further down there is a reference to Equation 7, however probably it is meant to be 8 and even that with some leap in notation).
The paper would benefit from better proof-reading, where mistakes in a very dense argumentation make it hard to follow (e.g. I do not understand the sentence “The K-learning expectation (7) is with respect to the posterior over Q[…] to give a parametric approximation to the probability of optimality.”)
Literature wise, the paper draws heavily from two unpublished papers (Levine, 2018; O’Donoghue, 2018). While this makes it harder to arrive at a high confidence level with respect to the paper’s claims, I would not argue this to be critical.
I would consider raising my score, if the authors would improve the accessibility of the paper by polishing the argumentation and notation. 
Confidence: low. It is very likely, that I have misunderstood key arguments and derivations. Also, I did not attempt to follow all of the technical derivations.
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post rebuttal comment:
I changed the score of my review in light of the rebuttal.
The changes made to the paper overall address my concerns.
I do consider the additional explanations and re-phrasings as well as the improved notation a nice improvement of the paper.
While I did not read all of the appendix, Section 5.1 is much more readable and understandable in the new version.
In light of this paper probably being published, I share some typos/inconsistencies I still noticed:
p. 4: the solving for -> solving for the
p. 7: s_{h+1} -> s' (in Table 3) ?
p. 7: table -> Table; tables -> Tables
p. 7: (Fix position of K:) \pi_h(s)^K -> \pi_h^K(s) ((also in Appendix))
p. 9: (2x) soft-Q learning -> soft Q-learning; Q Networks -> Q-Networks; Soft Q -> soft Q-learning