Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper provides an error analysis of deep-fitted Q-iteration (DFQI) applying ReLU-activated feed-forward neural networks in reinforcement learning. This analysis seeks to improve a previous study by considering more relevant variables such as the ambient dimension, the number of samples, the width and depth of the neural network, and the number of training iterations.
strengths_and_weaknesses: *Strengths:
- The paper is well-written and structured
- The paper provides insights into hyperparameters setting when training DFQI to achieve a desired convergence rate.
- The authors analyzed the error with detailed proofs step by step (statistical error, approximation error, etc). 

*Weaknesses: 
- The study doesn't describe if there are limitations and what would be the future work.
suggested_changes: *Suggested Changes
- I recommend briefly explaining why ReLU was chosen and not other activation function.
- I suggest briefly highlighting the impact of this study in the abstract.
review_title: null

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 studies the error analysis of the deep-fitted Q-iteration (DFQI) with value functions approximation using ReLU networks in batch RL. The bound depends on the sample complexity, the ambient dimension polynomially, the width and depth of the neural network.
strengths_and_weaknesses: Strengths
- The paper is generally well-written. 
- The theoretical results improved upon existing work of Fan et al. (2020) by introducing a weaker $\alpha-$mixing condition; the error bound is also improved by considering the sample complexity and reducing the dependency on the dimension d from exponential to polynomial. Overall, I consider this result to be strong.

Weakness
- It would be helpful if the authors include a Table to summarize the notations used in this work.
- The $\alpha-$mixing condition should be stated in the main paper.
suggested_changes: Please see Weakness
review_title: null

Decision: Invite to present