AGENTIC-SYSTEMSJuly 15, 20261 min read

Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses

Researchers have proposed a calibration-first reward audit framework to optimize reinforcement learning (RL) control in smart greenhouses. This framework decomposes complex scalar rewards into specific components like temperature, CO2, and humidity, allowing engineers to better monitor and fine-tune climate control policies.

Computer Science > Artificial Intelligence

Title:Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses

View PDF HTML (experimental)Abstract:Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone. For smart-greenhouse control, however, a single simulator return is not enough: a grower or control engineer also needs to know when the policy heats, enriches CO2, vents, manages humidity, deploys screens, or uses this http URL propose a reproducible calibration-first reward audit framework that keeps named greenhouse-control reward components comparable across simulator training, facility-adapted rollouts, logged Autonomous Greenhouse Challenge records, and actuator-rule distillation. In GreenLight-Gym, the framework decomposes the scalar reward into conditional temperature, CO2, humidity and vapor-pressure-deficit, screen, and actuation-proxy terms; adapts GreenLight to the second Autonomous Greenhouse Challenge logged climate traces; and scores the same components on logged greenhouse data.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Good Benchmarks

As AI models advance rapidly, establishing high-quality benchmarks is more critical than ever. A recent paper outlines the core criteria that define a truly effective and practical AI evaluation system.

agentic-systems

Cost-Governed RAG: Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems

Researchers have introduced Cost-Governed RAG, a novel architecture that enables precise, per-tenant cost attribution across both retrieval and generation layers in multi-tenant LLM systems. By leveraging a codebook-oblivious vector index called TurboVec, the system achieves 99.96% cost attribution accuracy while reducing infrastructure costs by up to 9 times.

agentic-systems

The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning

This paper explores the paradigm shift enabled by Geospatial Foundation Models (GeoFMs), detailing their types, operationalization strategies, and the future of Agentic Geospatial Reasoning.

NOW LET US Related – Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking

agentic-systems

Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking

This paper presents GenAI Evaluation, a governed, configuration-driven pipeline designed for large-scale, multi-dimensional evaluation of retail conversational agents. The framework processes approximately 50,000 daily records, achieving high alignment with human judgment across key metrics like helpfulness, truthfulness, and tone.

agentic-systems

Representing and Generating Levels Over Time through Playtrace Reconstructive Partitioning

Researchers introduce a novel 'cake' representation and the Playtrace Reconstructive Partitioning (PRP) algorithm, allowing AI to generate game levels by encoding dynamic playtrace information over time. Tested on Sokoban, this approach outperforms existing PCG methods while preserving solution diversity.

agentic-systems

Connected by Construction: Learning Tractable Near-Tour Marginals for Traveling Salesman Problems

Researchers have introduced C2TSP, a breakthrough unsupervised learning pipeline for the Traveling Salesman Problem (TSP). By focusing on learning structurally meaningful latent objects rather than relying solely on the final decoding stage, C2TSP opens up new possibilities for AI-driven combinatorial optimization.