# Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks

arXiv: https://arxiv.org/abs/2609.34966 · HTML: https://mlsanity.com/paper/2609.34966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 40 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Safe Reinforcement Learning in Constrained Markov Decision Processes](https://mlsanity.com/paper/2008.06626/) (2020)
- [Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization](https://mlsanity.com/paper/2003.01303/) (2020)
- [A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors](https://mlsanity.com/paper/2302.13137/) (2023)
- [A Survey of Safe Reinforcement Learning and Constrained MDPs: A Technical Survey on Single-Agent and Multi-Agent Safety](https://mlsanity.com/paper/2505.17342/) (2025)
- [Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Control](https://mlsanity.com/paper/2303.06110/) (2023)
- [Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control](https://mlsanity.com/paper/2409.12789/) (2024)
- [Kolmogorov-Arnold Network for Online Reinforcement Learning](https://mlsanity.com/paper/2408.04841/) (2024)
- [ROSARL: Reward-Only Safe Reinforcement Learning](https://mlsanity.com/paper/2306.00035/) (2023)
- [Coordinated Frequency Control through Safe Reinforcement Learning](https://mlsanity.com/paper/2202.00530/) (2022)
- [Safety Representations for Safer Policy Learning](https://mlsanity.com/paper/2502.20341/) (2025)
- [A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions](https://mlsanity.com/paper/2508.09128/) (2025)
- [Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control](https://mlsanity.com/paper/2505.23355/) (2025)

## Abstract (arXiv)

Greenhouse climate control balances economic return with maintaining temperature, humidity and CO2 within crop-adapted growth ranges. Conventional reinforcement learning (RL) greenhouse controllers use fixed reward penalties to limit climate constraint violations, yet such heuristic penalties cannot explicitly constrain long-term cumulative violations. Poorly tuned weights either lead to overly conservative policies and lower yields, or fail to suppress persistent climate deviations that harm photosynthesis and induce crop diseases. To address this issue, we formulate greenhouse climate regulation as a Constrained Markov Decision Process (CMDP) and use a Lagrangian safe RL framework RCPO-PPO to separate economic optimization and cumulative safety constraints, enabling adaptive penalty adjustment without manual tuning. To handle strong nonlinear, time-varying coupling between greenhouse microclimate and crop growth, Kolmogorov-Arnold Networks (KANs) replace Multi-Layer Perceptrons (MLPs) as policy and value approximators for improved nonlinear representation. Sinusoidal cyclic time features are embedded in observations to capture diurnal environmental periodicity. Simulations use a classic winter lettuce greenhouse model driven by 40-day real weather disturbances. Compared with vanilla penalty-based PPO, our method cuts cumulative climate violations by 18.65% and raises lettuce economic profit by 2.91%, keeping violations stable near the safety threshold. This decoupled CMDP optimization with KAN-based policy representation mitigates long-term climate risks and boosts planting profits, offering a constraint-aware control strategy for precision greenhouse cultivation.