DeepPolicy is a provider of autonomous optimization industrial agent systems, focused on delivering software, hardware, and solutions for process industry, energy, semiconductor, and refrigeration systems, powered by reinforcement learning technology for engineering applications. Incubated at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, DeepPolicy has provided agent products and technical services to multiple industry-leading enterprises.
Core Products
A proprietary reinforcement learning training and deployment platform for industrial process control, built on robust disturbance-resistant, high-efficiency RL technology to ensure convergence in complex environments.
A platform for physics simulation, data-driven modeling, and small-scale industrial validation, ensuring every policy is thoroughly, quantifiably, and traceably verified before entering production.
An AI control terminal for industrial field execution — a dedicated controller for ReinforceOS deployment with powerful real-time control capabilities.
Industry Scenes
Across petrochemical, material chemistry, paper manufacturing, and refrigeration systems, each real-world site feeds operating conditions back into the algorithms, continuously refining the engineering boundaries of RL.
Industrial agents aggregate tens of millions of data points from plant zones, pipelines, and logistics in real time, dynamically optimizing reaction temperature, pressure, and feedstock ratios while ensuring safety interlocks, improving product quality consistency, reducing energy consumption and carbon emissions, and providing early warnings for leaks and corrosion.
Industrial agents analyze multi-variable coupling across batching, reaction, separation, purification, drying, and calcination processes to auto-optimize formulations and process parameters, reducing batch quality variance and shortening the cycle from pilot to mass production.
Industrial agents coordinate pulping, stock preparation, drying, and reeling processes — moving beyond fixed expert rules to adjust consistency, machine speed, and steam usage in real time, stabilizing paper basis weight and moisture while reducing cost waste and dryer energy consumption.
Industrial agents combine cooling load prediction with environmental changes to autonomously control compressor start/stop, frequency, condensing pressure setpoints, and air outlet volume, keeping refrigeration efficiency on the optimal COP curve, maintaining stable space temperatures, and delivering anomaly diagnostic recommendations.
Deployment
For: industrial scenarios with data, optimization goals, and measurable results
System collects field process parameters and interacts directly with real production equipment — no simulation environment needed
Workstations connect to the self-learning platform via adapter software; human action review during learning ensures product quality
Human action review during learning ensures product quality
Supports SUPCON, HollySys, and multiple DCS systems
Industrial Gateway
Protocol Bridge
Edge Control Terminal
Field Device Integration
DCS Controller
Profibus-DP/PA Protocol
Real Environment
Collect field process parameters, interact directly with production devices
ReinforceOS Self-learning Platform
Workstation integration with human review during learning phase
Industrial Gateway → Edge Terminal
Field device integration, supports multiple DCS systems
DCS Controller
Profibus-DP/PA closed-loop protocol interaction
Comparison
INNOVATION
Addressing key control challenges in complex industrial processes, DeepPolicy continuously advances R&D in industrial agents, edge-cloud collaboration, and optimization algorithm platforms — bringing innovation into production.
AI AGENT
Building integrated industrial agents with perception, analysis, decision-making, and execution capabilities for complex conditions — shifting control systems from reactive response to proactive optimization.
EDGE CLOUD
Bridging cloud model capabilities with edge real-time control, ensuring response speed and system stability while supporting continuous iteration across multiple scenarios.
OPTIMIZATION
Integrating dynamic knowledge graphs, operations research, and process mechanism modeling to continuously improve energy efficiency, quality, and control precision in complex industrial processes.
INDUSTRIAL ECOSYSTEM
DeepPolicy continuously connects research resources, hands-on experience, and industry scenarios around the real needs of complex industrial control — driving an efficient closed loop from R&D to field validation to commercial deployment.
RESEARCH
Leveraging CAS and other research resources to continuously strengthen technical research and capability transfer in industrial agents and optimization control.
PRACTICE
Deep deployment in factory frontlines, accumulating reusable field experience to accelerate product deployment.
SCENARIO
Through deep technical engagement with industry leaders, continuously accumulating validation experience and application loops under real operating conditions.
Research
Practice
Industry
From research breakthroughs to field practice, to industrial validation — DeepPolicy is building a long-term collaborative ecosystem for complex industrial processes.