Venue: NeurIPS 2021 Track Datasets_and_Benchmarks Round1

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: summary and contributions: This paper proposes a new method for RL in learning to shape plants. The benchmark proposes several tasks in growing plants that involve challenges in control, hierarchical learning, fairness, and multi-objective learning. The authors evaluate PPO on the proposed environment in the four problems described above.
strengths: The paper presents an OpenAI Gym-compatible environment for using RL to grow plants, which makes it easy for the community to use. The proposed benchmark tackles a real-world problem, which is of great significance to real-world applications of RL algorithms.
weaknesses: The proposed benchmark is a bit artificial, especially the tasks on growing plants into shapes of MNIST digits. All of the environments are constructed in simulation, which makes me uncertain about the impact on growing plants in the real world. Moreover, the evaluations only consider PPO, which seems rather limited.
correctness: Yes
clarity: Yes
relation to prior work: Yes
additional feedback: See above
review_title: Official Review

final_score: rating: 7: Good paper, accept
review_content: summary and contributions: The paper proposed GrowSpace, a RL environment based on OpenAI gym that enables the control of procedurally generated plant structures. The provided benchmark can simulate the branch based on photons provided by the light source, so users can leverage this environment to train smart agents for growing planets towards specific goals.

The proposed benchmark has 4 different challenges including encompass control, hierarchical learning, fairness, and multi-objective learning. And the authors provide baseline agents using Proximal Policy Optimization (PPO) and a case study of the behavior and weaknesses of the agents.

strengths: The paper is well-written, and the idea of creating a benchmark for evaluating the growth of the planet is novel and beneficial to the agriculture and AI community.

The environment is open-sourced and based on OpenAI gym, so the users can easily experiment with many baseline RL algorithms.

The environment leverages the Space Colonization Algorithm for the branching based on the light source, which can simulate the branching in the (ideal) real-world environment.

The environment is evaluated with baseline RL algorithms and also a case study.
weaknesses: The simulation is still very simple, and doesn't support multiple variables in real life such as temperature, light strength, wind etc.

As a simulation, it would be more persuadable if the author can provide real-world experiments, even with the simplest settings. 
correctness: Yes, the simulation is based on Space Colonization Algorithm for branching, and the metrics/rewards for the 4 tasks Grow Plant to Goal, Find Plants, Grow Multiple Plants and Grow Plant to Shape all look correct to me.
clarity: Yes the paper is well written
relation to prior work: Yes
additional feedback: na
review_title: A novel environment that connects agriculture with AI

final_score: rating: 5: Marginally below acceptance threshold
review_content: summary and contributions: Paper presents an interesting problem domain treating plants as dynamical systems, formulates benchmark tasks around them to study control, hierarchical learning, fairness and multi-objective learning. Authors also provide baselines and case studies. 


strengths: - An interesting problem domain treating plants as dynamical systems and the tasks of shaping them properly to grow habitable spaces
- Well written paper, clear to understand and thorough in their claims and experiments
- Authors are modest about the claims, highlight the weakness, and are candid about the shortcomings. I really cherished your points. #kudos 
weaknesses: - Unfortunately the problem space too narrow to be generally useful to the community
- If I instead evaluate the main contribution to be around benchmarks to evaluate hierarchal learning, fairness, and multi-objective learning, the scope and the technical rigor aren't up to the mark to carefully evaluate the fundamental issues of these domains
- The original motivation of providing real world tasks is well justified, but as the authors themselves noted -- neither the simulations nor the final policies are validated by real world data or experiments. Additionally the underlying problem studied is an over simplification of the real world problem.
correctness: - I found the experiment designs and evaluations to be well motivated and designed 
- Line 95-96: The claim that  robotics simulations with MuJoCo can't encompass challenges with fairness and multi-objectivity is just not true. On one hand these challenges aren't modelling issues but task formulation issues. On the other hand, there are multiple well established tasks in the RL community that deals with multi-objective problems (almost all RL continues control tasks are multi-objective [1][2])

[1] Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations, Rajeswaran, Kumar et al. 
[2] https://meta-world.github.io/


 

clarity: Over all the paper is well motivated and presented. A few suggestions below 
- It was hard to initially follow how different tasks cater of different challenges. Possibly providing some context while introducing the task could help.
- Line 317 - How is this order established?
- Unclear what the two kinds of observations are and why they are useful, perhaps a picture can say a thousand words here

relation to prior work: - paper can use a bit better positioning with respect to continuous control reinforcement learning literature and benchmarking.
additional feedback: - I really like how candid authors are with their claims, limitations, and weaknesses. I hope to see more researchers will set similar examples to follow upon.
- One of the most interesting problem setting I have seen in RL benchmarking lately.
review_title: An interesting problem setting but perhaps too narrow to be generally useful 

Meta-review: While this paper proposes an interesting setting of growing a shape plant, reviewers agree that it is unclear how this simplistic setting with no real world data would add to the community. While simplistic settings are used by many, further distinction from ones already provided by OpenAI gym would be necessary. Reviewers also argue that in order to overcome this, even a simplest setting real-world data or experiments could bring this work a lot further. We also recommend mentioning other evaluations authors did (but moved to appendix) into the main text to avoid confusion. 

Decision: Reject