Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: This paper proposes extending the recently proposed Forward-Forward (FF) algorithm to imitation learning. Results on imitating an expert PPO agent in typical toy environments show that an FF model can match a back-propagation model with sufficient samples. Future work includes development of FF towards online RL and continuous action spaces.
strengths_and_weaknesses: Strengths:
* All claims and conclusions are well-justified with experimental results.
* Relevant literature is cited appropriately.
* The follow-up work is clear and promising.
* The paper is easy to read and grammatically well-written, except for some typos.
* The submission adheres to formatting requirements and the ICLR code of conduct.



Weaknesses:
* Some crucial definitions and explanations are missing from the methods and results. 
* There are some minor typos that could have been caught with quick proofreading.
suggested_changes: Some explanations and definitions are missing. What is the architecture of each model? You mention the FF and BP models are MLPs, but don't mention further details such as network depth, layer widths, or nonlinearities, nor the details of the PPO expert. How were the architectures and hyperparameters selected? Then, in Table 1, I infer that the row "Dataset" means "The proportion of the dataset used", but this should be explicitly described. How were these sampled? Is each average/standard deviation measured across multiple models, or across multiple tests of the same agent?

This paper could benefit from a quick proofreading. There are a few typos: for example, in the first paragraph of Section 4, "...and the *back-propagation* (BP) algorithms...", "Therefore, ~Thus,~ both models...", and "*T*he experience dataset...". Further, ensure consistency of past vs. present tense: for example, Section 3 switches back and forth between tenses multiple times.
review_title: null

Meta-review: metareview: Strengths
- Clear, organized and discusses relevant literature
- Claims match experiments

Weaknesses
- Important definitions are missing from the methods
- Insufficient details are included for reproducibility of experimental results; experiments only on two data sets.
- The negative result for a new algorithm for a new application should be complemented/compared with its original application
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to archive