Machine learning algorithms for controlling devices will need to learn quickly, with few trials. Such a goal can be attained with concepts borrowed from continental philosophy and formalized using tools from the mathematical theory of categories. Illustrations of this approach are presented on a cyberphysical system: the slot car game, and also on Atari 2600 games.
The authors argue that many machine learning systems need a large amount of data and long training times.  To mend those shortcomings their proposed algorithm takes the novel approach of combining mathematical category theory and continental philosophy.  Instead of computation units, the concept of entities and a 'me' is introduced to solve reinforcement learning tasks on a cyber-physical system as well as the Atari environment. This allows for an AI that is understandable for humans at every step of the computation in comparison to the 'black box learning of an neural network. 
 
 
Positives:
	•	Novel approach towards more explainable and shorter training times/ less data
	•	Solid mathematical description in part 3.3
	•	Setup well explained
 
 
Negatives:
	•	Use of colloquial language (the first sentence of the abstract alone contains the word 'very' twice)
	•	Some paragraphs are strangely structured
	•	Incoherent abstract 
	•	Only brief and shallow motivation given (No evidence to support the claim)
	•.      Brief and therefore confusing mention of methods 
	•	No mention of results
	•	Very short in general
	•	Many grammatical errors (wrong tense use, misuse of a/an,... )
	•	Related Work is either Background or an explanation of the two test systems. While related approaches in those systems are also provided, the section is mainly used to introduce the test beds 
	•	No direct comparison between algorithm and existing methods is given. It is stated that some extra measures from other measures such as sensors are not used and that it learns to rank with a human in under a minute. However, many questions remain unanswered: But how good is this? How long do other systems need? Is this a valid point to raise? What score functions do other papers use?
	•	2.2: Title choice could have been  more descriptive of the subsection. 'Video Games' indicates a broader analysis of RL in any game but the section mainly restricts itself to the Atari Environment
	•	While many methods are mentioned they are not set in context but only enumerated. Many concepts are only named without explanation or how they fit into the picture the authors are trying to paint.
 
	•	A clear statement of the hypothesis and reason/motivation behind pursuing this approach is missing. Information is indirectly given in the third section where the point is raised that the approach was chosen in contrast to 'black box NNs'. This seems to be a very crucial point that could have been highlighted more. The achieved result are by no means comparable to the NN approaches but they are faster and explainable for a human. 
	•	Dreyfus' criticism of AI is presented as the key initiator for this idea. Ideas by other authors that utilise this criticism as their foundation are conceptually similar, they could have therefore been mentioned in the related work section.
	•	The paper fails to mention the current movement in the AI community to make AI more explainable. One of their two key advantages seems to be that they develop a more intuitive explainable system. However, this movement is completely ignored and not given a single mention. The paper, therefore,  does not set their approach in context and is not able to acknowledge related work in this area. 
	•	The section about continental based philosophy is rather confusing
	•	Instead of explaining the philosophy, analytical philosophy is described in details and continental philosophy is only described as not following analytical patterns. A clear introduction to this topic is missing.
	•	When described, it is stated that it's a mix of different German and French doctrines that are name dropped but not explained\ and leave the reader confused.
	•	Result section not well structured and results lack credibility:
	•	Long sections in the result section describe the actual algorithm. This should have been discussed before the results.
	•	Results for slot car are not convincing:
	•	Table 1 only shows the first the last and the best lap (and in most of them the human is better) 
	•	Not even an average measure is given only samples. This is very suspicious.
	•	Why the comparison with DQN and only DQN? How was this comparison initialised? Which parameters were used? Neither is the term DQN resolved as Deep Q-Network nor is any explanation given. There are many methods/method classes performing RL on the Atari Environment. The mention of only one comparison  leaves reasonable doubt about the claim that the system learns faster.   
 
SUMMARY: Reject. Even though the idea presented is a novel contribution and has potential the paper itself is highly unstructured and confusing and lacks a proper grammar check. No clear hypothesis is formed until section 3. The concept of Explainable AI which could have been a good motivation does not find any mentioning. Key concepts such as continental philosophy are not explained in a coherent way. The results are presented in a questionable way. As the idea is promising it is recommended to the authors to restructure the paper and conduct more experiments to be able to get accepted.