mlsanityData to 7 Oct 2026

Towards Fully Automated Decision-Making Systems for Greenhouse Control: Challenges and Opportunities

arXiv 2503.21640 ↗First posted 27 Mar 2025cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Machine learning has been successful in building control policies to drive a complex system to desired states in various applications (e.g. games, robotics, etc.). To be specific, a number of parameters of policy can be automatically optimized from the observations of environment to be able to generate a sequence of decisions leading to the best performance. In this survey paper, we particularly explore such policy-learning techniques for another unique, practical use-case scenario--farming, in which critical decisions (e.g., water supply, heating, etc.) must be made in a timely manner to minimize risks (e.g., damage to plants) while maximizing the revenue (e.g., healthy crops) in the end. We first provide a broad overview of latest studies on it to identify not only domain-specific challenges but opportunities with potential solutions, some of which are suggested as promising directions for future research. Also, we then introduce our successful approach to being ranked second among 46 teams at the ''3rd Autonomous Greenhouse Challenge'' to use this specific example to discuss the lessons learned about important considerations for design to create autonomous farm-management systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 47 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

24 of 47 references.

References · 24

  1. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  2. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  3. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 753
  4. 2018A Tutorial on Bayesian Optimizationcited by 734
  5. 2016RL2: Fast Reinforcement Learning via Slow Reinforcement Learningcited by 490
  6. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 259
  7. 2021Reinforcement learning based recommender systems: A surveycited by 187
  8. 2019IPO: Interior-point Policy Optimization under Constraintscited by 119
  9. 2020Learning to Utilize Shaping Rewards: A New Approach of Reward Shapingcited by 112
  10. 2019Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learningcited by 102
  11. 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 96
  12. 2019Improving Generalization in Meta Reinforcement Learning using Learned Objectivescited by 68
12 more
  1. 2018Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learningcited by 35
  2. 2020Optimal Thompson Sampling strategies for support-aware CVaR banditscited by 26
  3. 2023Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shapingcited by 25
  4. 2021CropGym: a Reinforcement Learning Environment for Crop Managementcited by 20
  5. 2019Stochastic Bandits with Context Distributionscited by 18
  6. 2021Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensingcited by 12
  7. 2019Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learningcited by 7
  8. 2021Cross Domain Robot Imitation with Invariant Representationcited by 7
  9. 2021CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicingcited by 7
  10. 2021Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomaliescited by 6
  11. 2021IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Controlcited by 5
  12. 2025Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learningcited by 3

Built on by

Not cited yet.