mlsanityData to 14 Oct 2026

WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management Strategies

arXiv 2502.19308 ↗First posted 26 Feb 2025cs.AIcited by 4

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

We introduce WOFOSTGym, a novel crop simulation environment designed to train reinforcement learning (RL) agents to optimize agromanagement decisions for annual and perennial crops in single and multi-farm settings. Effective crop management requires optimizing yield and economic returns while minimizing environmental impact, a complex sequential decision-making problem well suited for RL. However, the lack of simulators for perennial crops in multi-farm contexts has hindered RL applications in this domain. Existing crop simulators also do not support multiple annual crops. WOFOSTGym addresses these gaps by supporting 23 annual crops and two perennial crops, enabling RL agents to learn diverse agromanagement strategies in multi-year, multi-crop, and multi-farm settings. Our simulator offers a suite of challenging tasks for learning under partial observability, non-Markovian dynamics, and delayed feedback. WOFOSTGym's standard RL interface allows researchers without agricultural expertise to explore a wide range of agromanagement problems. Our experiments demonstrate the learned behaviors across various crop varieties and soil types, highlighting WOFOSTGym's potential for advancing RL-driven decision support in agriculture.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 83 references that are in the map. First posted on arXiv 26 Feb 2025.

Builds on

20 of 83 references.

References · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  5. 2016Generative Adversarial Imitation Learningcited by 1,597
  6. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  7. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  8. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  9. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
  10. 2024NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarkingcited by 354
  11. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 319
  12. 2016Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learningcited by 286
8 more
  1. 2019Active Domain Randomizationcited by 170
  2. 2018Multi-task Deep Reinforcement Learning with PopArtcited by 141
  3. 2022imitation: Clean Imitation Learning Implementationscited by 34
  4. 2021CropGym: a Reinforcement Learning Environment for Crop Managementcited by 21
  5. 2022Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulationscited by 20
  6. 2022gym-DSSAT: a crop model turned into a Reinforcement Learning environmentcited by 13
  7. 2022Optimizing Crop Management with Reinforcement Learning and Imitation Learningcited by 13
  8. 2023A SWAT-based Reinforcement Learning Framework for Crop Managementcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2026Toward Self-Adaptive Physical AI: Can LLM Agents Manage Long-Horizon Physical Tasks?cited by 1
  2. 2025Uncovering Systemic and Environment Errors in Autonomous Systems Using Differential Testingcited by 0
  3. 2025Calibrating Biophysical Models for Grape Phenology Prediction via Multi-Task Learningcited by 0
  4. 2026A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024The New Agronomists: Language Models are Experts in Crop Management
  2. 2024AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning
  3. 2024A Comparative Study of Deep Reinforcement Learning for Crop Production Management
  4. 2024Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm
  5. 2024Learning-based agricultural management in partially observable environments subject to climate variability
  6. 2025To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning
  7. 2024CROPS: A Deployable Crop Management System Over All Possible State Availabilities
  8. 2024Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties